diff --git a/README.md b/README.md index 011b691..7b0bbfc 100644 --- a/README.md +++ b/README.md @@ -23,14 +23,25 @@ Designed initially for evaluating the impacts of **COVID-19**, `DFMDash` is flex - **Dynamic Factor Models**: Build models that combine pandemic and economic series to estimate latent variables representing pandemic intensity. - **Drag-and-Drop**: Drop in files - options are then dynamically generated from the input data. + ## Installation -There are multiple ways to run `DFMDash`, +There are multiple ways to install and run `DFMDash`. + +> **Note**: Due to PyPI constraints, the example data files are stored on the GitHub repository rather than in the pip-installed package. If you wish to use `DFMDash` with the provided example data, please clone the repository and follow the installation steps below. ### Prerequisites - Python 3.10+ is required. - Tested environments: **Ubuntu**, **WSL2 (Windows)**, **MacOS** (M1 compatible). +### Option 0: Using Pip +> **Advanced:** If you have a Python environment set up, prefer to install via `pip` and _do not_ want/need the example data. + +1. Install the package: + ```bash + pip install dfmdash + ``` + ### Option 1: Using Poetry 0. [Install Poetry](https://python-poetry.org/) @@ -175,3 +186,11 @@ For larger changes, please open an issue for discussion before submitting a PR. ## License `DFMDash` is distributed under the MIT License. See [LICENSE](./LICENSE) for details. + + +# Citation +> If you use this tool in your research, please cite the following paper + +``` +Cooke, A., & Vivian, J. (2024). Pandemic Intensity Estimation using Dynamic Factor Modelling. Statistics, Politics and Policy. Manuscript under review. +``` diff --git a/coverage.xml b/coverage.xml index f7f3de8..d6bfc42 100644 --- a/coverage.xml +++ b/coverage.xml @@ -1,12 +1,12 @@ - + - /home/jvivian/covid19-drDFM/covid19_drdfm + /home/jvivian/covid19-drDFM/dfmdash - + @@ -114,7 +114,7 @@ - + @@ -158,10 +158,10 @@ - - - - + + + + diff --git a/dfmdash/data/example-data/full-2019-global-2/AK/df.csv b/data/example-data/full-2019-global-2/AK/df.csv similarity index 100% rename from dfmdash/data/example-data/full-2019-global-2/AK/df.csv rename to data/example-data/full-2019-global-2/AK/df.csv diff --git a/dfmdash/data/example-data/full-2019-global-2/AK/raw.csv b/data/example-data/full-2019-global-2/AK/raw.csv similarity index 100% rename from dfmdash/data/example-data/full-2019-global-2/AK/raw.csv rename to data/example-data/full-2019-global-2/AK/raw.csv diff --git a/dfmdash/data/example-data/full-2019-global-2/AK/run-info.yaml b/data/example-data/full-2019-global-2/AK/run-info.yaml similarity index 100% rename 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rename to data/processed/data.csv diff --git a/dfmdash/data/processed/data.h5ad b/data/processed/data.h5ad similarity index 100% rename from dfmdash/data/processed/data.h5ad rename to data/processed/data.h5ad diff --git a/data/processed/df_paths.txt b/data/processed/df_paths.txt new file mode 100644 index 0000000..9b6df4b --- /dev/null +++ b/data/processed/df_paths.txt @@ -0,0 +1,33 @@ +../data/raw/economic/GDP/gdp_current_dollars.csv +../data/raw/economic/UI/ar203_output.csv +../data/raw/economic/consumption/consumption.csv +../data/raw/economic/consumption_durable_goods/consumption_durable_goods.csv +../data/raw/economic/consumption_nondurable_goods/consumption_nondurable_goods.csv +../data/raw/economic/consumption_services/consumption_services.csv +../data/raw/economic/consumption_total_goods/consumption_total_goods.csv +../data/raw/economic/employment/CE16OV_output.csv +../data/raw/economic/employment/PAYEMS_output.csv +../data/raw/economic/inflation/CPIAUCSL_output.csv +../data/raw/economic/inflation/PCEPILFE_output.csv +../data/raw/economic/inflation/PCEPI_output.csv +../data/raw/economic/interest_rates/rates_output.csv +../data/raw/economic/investment/K1TTOTL1ES000_output.csv +../data/raw/economic/investment/RPFI_output.csv +../data/raw/economic/productivity/labor-productivity-by-state-and-region_output.csv +../data/raw/economic/unemployment/ststdsadata_output.csv +../data/raw/intervention/american_rescue_plan/fiscalrecoveryfunds-statefunding1-CSV_output.csv +../data/raw/intervention/cares/cares_state_allocation_output.csv +../data/raw/intervention/ppp/PPP_states_and_territories_output.csv +../data/raw/pandemic/cases/CDC_state_cases_and_deaths_output.csv +../data/raw/pandemic/cases/JHU_time_series_cases_output.csv +../data/raw/pandemic/deaths/time_series_covid19_deaths_US_output_FIXED - time_series_covid19_deaths_US_output.csv +../data/raw/pandemic/vaccinations/19_Vaccinations_in_the_United_States_Jurisdiction_output_output.csv +../data/raw/pandemic/vaccinations/time_series_covid19_vaccine_us_output.csv +../data/raw/state_mandates/UWashington/USstatesCov19distancingpolicyBETA_complete_output.csv +../data/raw/state_mandates/mask_mandates/U.S._State_and_Territorial_Public_Mask_Mandates_From_April_8__2020_through_August_15__2021_by_State_by_Day_output.csv +../data/raw/economic/inflation/pcpiMvMd.csv +../data/raw/economic/interest_rates/FEDFUNDS_output.csv +../data/raw/economic/inflation/pcpiMvMd.csv +../data/raw/economic/interest_rates/FEDFUNDS_output.csv +../data/raw/state_mandates/shelter_in_place/U.S._State_and_Territorial_Stay-At-Home_Orders__March_15__2020___August_15__2021_by_County_by_Day_output.csv +../data/raw/pandemic/cases/CDC_Case_Surveillance.csv diff --git a/dfmdash/data/processed/factors-backup.json b/data/processed/factors-backup.json similarity index 100% rename from dfmdash/data/processed/factors-backup.json rename to data/processed/factors-backup.json diff --git 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data/raw/economic/GDP/gdp_current_dollars.csv diff --git a/dfmdash/data/raw/economic/UI/ar203_output.csv b/data/raw/economic/UI/ar203_output.csv similarity index 100% rename from dfmdash/data/raw/economic/UI/ar203_output.csv rename to data/raw/economic/UI/ar203_output.csv diff --git a/dfmdash/data/raw/economic/consumption/consumption.csv b/data/raw/economic/consumption/consumption.csv similarity index 100% rename from dfmdash/data/raw/economic/consumption/consumption.csv rename to data/raw/economic/consumption/consumption.csv diff --git a/dfmdash/data/raw/economic/consumption_durable_goods/consumption_durable_goods.csv b/data/raw/economic/consumption_durable_goods/consumption_durable_goods.csv similarity index 100% rename from dfmdash/data/raw/economic/consumption_durable_goods/consumption_durable_goods.csv rename to data/raw/economic/consumption_durable_goods/consumption_durable_goods.csv diff --git 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data/raw/economic/consumption_total_goods/consumption_total_goods.csv diff --git a/dfmdash/data/raw/economic/employment/CE16OV_output.csv b/data/raw/economic/employment/CE16OV_output.csv similarity index 100% rename from dfmdash/data/raw/economic/employment/CE16OV_output.csv rename to data/raw/economic/employment/CE16OV_output.csv diff --git a/dfmdash/data/raw/economic/employment/PAYEMS_output.csv b/data/raw/economic/employment/PAYEMS_output.csv similarity index 100% rename from dfmdash/data/raw/economic/employment/PAYEMS_output.csv rename to data/raw/economic/employment/PAYEMS_output.csv diff --git a/dfmdash/data/raw/economic/inflation/CPIAUCSL_output.csv b/data/raw/economic/inflation/CPIAUCSL_output.csv similarity index 100% rename from dfmdash/data/raw/economic/inflation/CPIAUCSL_output.csv rename to data/raw/economic/inflation/CPIAUCSL_output.csv diff --git a/dfmdash/data/raw/economic/inflation/PCEPILFE_output.csv b/data/raw/economic/inflation/PCEPILFE_output.csv similarity index 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b/data/raw/factors.json similarity index 100% rename from dfmdash/data/raw/factors.json rename to data/raw/factors.json diff --git a/dfmdash/data/raw/intervention/american_rescue_plan/fiscalrecoveryfunds-statefunding1-CSV_output.csv b/data/raw/intervention/american_rescue_plan/fiscalrecoveryfunds-statefunding1-CSV_output.csv similarity index 100% rename from dfmdash/data/raw/intervention/american_rescue_plan/fiscalrecoveryfunds-statefunding1-CSV_output.csv rename to data/raw/intervention/american_rescue_plan/fiscalrecoveryfunds-statefunding1-CSV_output.csv diff --git a/dfmdash/data/raw/intervention/cares/cares_state_allocation_output.csv b/data/raw/intervention/cares/cares_state_allocation_output.csv similarity index 100% rename from dfmdash/data/raw/intervention/cares/cares_state_allocation_output.csv rename to data/raw/intervention/cares/cares_state_allocation_output.csv diff --git a/dfmdash/data/raw/intervention/ppp/PPP_states_and_territories_output.csv b/data/raw/intervention/ppp/PPP_states_and_territories_output.csv similarity index 100% rename from dfmdash/data/raw/intervention/ppp/PPP_states_and_territories_output.csv rename to data/raw/intervention/ppp/PPP_states_and_territories_output.csv diff --git a/dfmdash/data/raw/outfile.parq b/data/raw/outfile.parq similarity index 100% rename from dfmdash/data/raw/outfile.parq rename to data/raw/outfile.parq diff --git a/dfmdash/data/raw/pandemic/cases/CDC_Case_Surveillance.csv b/data/raw/pandemic/cases/CDC_Case_Surveillance.csv similarity index 100% rename from dfmdash/data/raw/pandemic/cases/CDC_Case_Surveillance.csv rename to data/raw/pandemic/cases/CDC_Case_Surveillance.csv diff --git a/dfmdash/data/raw/pandemic/cases/CDC_Case_Surveillance_Fixed - CDC_Case_Surveillance.csv b/data/raw/pandemic/cases/CDC_Case_Surveillance_Fixed - CDC_Case_Surveillance.csv similarity index 100% rename from dfmdash/data/raw/pandemic/cases/CDC_Case_Surveillance_Fixed - CDC_Case_Surveillance.csv rename to data/raw/pandemic/cases/CDC_Case_Surveillance_Fixed - CDC_Case_Surveillance.csv diff --git a/dfmdash/data/raw/pandemic/cases/CDC_state_cases_and_deaths_output.csv b/data/raw/pandemic/cases/CDC_state_cases_and_deaths_output.csv similarity index 100% rename from dfmdash/data/raw/pandemic/cases/CDC_state_cases_and_deaths_output.csv rename to data/raw/pandemic/cases/CDC_state_cases_and_deaths_output.csv diff --git a/dfmdash/data/raw/pandemic/cases/COVID-19_Case_Surveillance_Public_Use_Data_with_Geography/confirmed_and_probable_case_analysis.Rmd b/data/raw/pandemic/cases/COVID-19_Case_Surveillance_Public_Use_Data_with_Geography/confirmed_and_probable_case_analysis.Rmd similarity index 100% rename from dfmdash/data/raw/pandemic/cases/COVID-19_Case_Surveillance_Public_Use_Data_with_Geography/confirmed_and_probable_case_analysis.Rmd rename to data/raw/pandemic/cases/COVID-19_Case_Surveillance_Public_Use_Data_with_Geography/confirmed_and_probable_case_analysis.Rmd diff --git a/dfmdash/data/raw/pandemic/cases/COVID-19_Case_Surveillance_Public_Use_Data_with_Geography/confirmed_and_probable_case_analysis.html b/data/raw/pandemic/cases/COVID-19_Case_Surveillance_Public_Use_Data_with_Geography/confirmed_and_probable_case_analysis.html similarity index 100% rename from dfmdash/data/raw/pandemic/cases/COVID-19_Case_Surveillance_Public_Use_Data_with_Geography/confirmed_and_probable_case_analysis.html rename to data/raw/pandemic/cases/COVID-19_Case_Surveillance_Public_Use_Data_with_Geography/confirmed_and_probable_case_analysis.html diff --git a/dfmdash/data/raw/pandemic/cases/COVID-19_Case_Surveillance_Public_Use_Data_with_Geography/confirmed_and_probable_cases_hospitalizations_deaths.csv b/data/raw/pandemic/cases/COVID-19_Case_Surveillance_Public_Use_Data_with_Geography/confirmed_and_probable_cases_hospitalizations_deaths.csv similarity index 100% rename from dfmdash/data/raw/pandemic/cases/COVID-19_Case_Surveillance_Public_Use_Data_with_Geography/confirmed_and_probable_cases_hospitalizations_deaths.csv rename to data/raw/pandemic/cases/COVID-19_Case_Surveillance_Public_Use_Data_with_Geography/confirmed_and_probable_cases_hospitalizations_deaths.csv diff --git a/dfmdash/data/raw/pandemic/cases/COVID-19_Case_Surveillance_Public_Use_Data_with_Geography/confirmed_cases_hospitalizations_deaths.csv b/data/raw/pandemic/cases/COVID-19_Case_Surveillance_Public_Use_Data_with_Geography/confirmed_cases_hospitalizations_deaths.csv similarity index 100% rename from dfmdash/data/raw/pandemic/cases/COVID-19_Case_Surveillance_Public_Use_Data_with_Geography/confirmed_cases_hospitalizations_deaths.csv rename to data/raw/pandemic/cases/COVID-19_Case_Surveillance_Public_Use_Data_with_Geography/confirmed_cases_hospitalizations_deaths.csv diff --git a/dfmdash/data/raw/pandemic/cases/JHU cases_time_series/JHU_case_output.csv b/data/raw/pandemic/cases/JHU cases_time_series/JHU_case_output.csv similarity index 100% rename from dfmdash/data/raw/pandemic/cases/JHU cases_time_series/JHU_case_output.csv rename to data/raw/pandemic/cases/JHU cases_time_series/JHU_case_output.csv diff --git a/dfmdash/data/raw/pandemic/cases/JHU_ cases_time_series/JHU_case_output.csv b/data/raw/pandemic/cases/JHU_ cases_time_series/JHU_case_output.csv similarity index 100% rename from dfmdash/data/raw/pandemic/cases/JHU_ cases_time_series/JHU_case_output.csv rename to data/raw/pandemic/cases/JHU_ cases_time_series/JHU_case_output.csv diff --git a/dfmdash/data/raw/pandemic/cases/JHU_time_series_cases_output.csv b/data/raw/pandemic/cases/JHU_time_series_cases_output.csv similarity index 100% rename from dfmdash/data/raw/pandemic/cases/JHU_time_series_cases_output.csv rename to data/raw/pandemic/cases/JHU_time_series_cases_output.csv diff --git a/dfmdash/data/raw/pandemic/deaths/time_series_covid19_deaths_US_output.csv b/data/raw/pandemic/deaths/time_series_covid19_deaths_US_output.csv similarity index 100% rename from dfmdash/data/raw/pandemic/deaths/time_series_covid19_deaths_US_output.csv rename to data/raw/pandemic/deaths/time_series_covid19_deaths_US_output.csv diff --git a/dfmdash/data/raw/pandemic/deaths/time_series_covid19_deaths_US_output_FIXED - time_series_covid19_deaths_US_output.csv b/data/raw/pandemic/deaths/time_series_covid19_deaths_US_output_FIXED - time_series_covid19_deaths_US_output.csv similarity index 100% rename from dfmdash/data/raw/pandemic/deaths/time_series_covid19_deaths_US_output_FIXED - time_series_covid19_deaths_US_output.csv rename to data/raw/pandemic/deaths/time_series_covid19_deaths_US_output_FIXED - time_series_covid19_deaths_US_output.csv diff --git a/dfmdash/data/raw/pandemic/vaccinations/19_Vaccinations_in_the_United_States_Jurisdiction_output_output.csv b/data/raw/pandemic/vaccinations/19_Vaccinations_in_the_United_States_Jurisdiction_output_output.csv similarity index 100% rename from dfmdash/data/raw/pandemic/vaccinations/19_Vaccinations_in_the_United_States_Jurisdiction_output_output.csv rename to data/raw/pandemic/vaccinations/19_Vaccinations_in_the_United_States_Jurisdiction_output_output.csv diff --git a/dfmdash/data/raw/pandemic/vaccinations/time_series_covid19_vaccine_us_output.csv b/data/raw/pandemic/vaccinations/time_series_covid19_vaccine_us_output.csv similarity index 100% rename from dfmdash/data/raw/pandemic/vaccinations/time_series_covid19_vaccine_us_output.csv rename to data/raw/pandemic/vaccinations/time_series_covid19_vaccine_us_output.csv diff --git a/dfmdash/data/raw/state_mandates/UWashington/USstatesCov19distancingpolicyBETA_complete_output.csv b/data/raw/state_mandates/UWashington/USstatesCov19distancingpolicyBETA_complete_output.csv similarity index 100% rename from dfmdash/data/raw/state_mandates/UWashington/USstatesCov19distancingpolicyBETA_complete_output.csv rename to data/raw/state_mandates/UWashington/USstatesCov19distancingpolicyBETA_complete_output.csv diff --git a/dfmdash/data/raw/state_mandates/UWashington/USstatesCov19distancingpolicyBETA_output.csv b/data/raw/state_mandates/UWashington/USstatesCov19distancingpolicyBETA_output.csv similarity index 100% rename from dfmdash/data/raw/state_mandates/UWashington/USstatesCov19distancingpolicyBETA_output.csv rename to data/raw/state_mandates/UWashington/USstatesCov19distancingpolicyBETA_output.csv diff --git a/dfmdash/data/raw/state_mandates/mask_mandates/U.S._State_and_Territorial_Public_Mask_Mandates_From_April_10__2020_through_August_15__2021_by_County_by_Day_output.csv b/data/raw/state_mandates/mask_mandates/U.S._State_and_Territorial_Public_Mask_Mandates_From_April_10__2020_through_August_15__2021_by_County_by_Day_output.csv similarity index 100% rename from dfmdash/data/raw/state_mandates/mask_mandates/U.S._State_and_Territorial_Public_Mask_Mandates_From_April_10__2020_through_August_15__2021_by_County_by_Day_output.csv rename to data/raw/state_mandates/mask_mandates/U.S._State_and_Territorial_Public_Mask_Mandates_From_April_10__2020_through_August_15__2021_by_County_by_Day_output.csv diff --git a/dfmdash/data/raw/state_mandates/mask_mandates/U.S._State_and_Territorial_Public_Mask_Mandates_From_April_8__2020_through_August_15__2021_by_State_by_Day_output.csv b/data/raw/state_mandates/mask_mandates/U.S._State_and_Territorial_Public_Mask_Mandates_From_April_8__2020_through_August_15__2021_by_State_by_Day_output.csv similarity index 100% rename from dfmdash/data/raw/state_mandates/mask_mandates/U.S._State_and_Territorial_Public_Mask_Mandates_From_April_8__2020_through_August_15__2021_by_State_by_Day_output.csv rename to data/raw/state_mandates/mask_mandates/U.S._State_and_Territorial_Public_Mask_Mandates_From_April_8__2020_through_August_15__2021_by_State_by_Day_output.csv diff --git a/dfmdash/data/raw/state_mandates/shelter_in_place/U.S._State_and_Territorial_Stay-At-Home_Orders__March_15__2020___August_15__2021_by_County_by_Day_output.csv b/data/raw/state_mandates/shelter_in_place/U.S._State_and_Territorial_Stay-At-Home_Orders__March_15__2020___August_15__2021_by_County_by_Day_output.csv similarity index 100% rename from dfmdash/data/raw/state_mandates/shelter_in_place/U.S._State_and_Territorial_Stay-At-Home_Orders__March_15__2020___August_15__2021_by_County_by_Day_output.csv rename to data/raw/state_mandates/shelter_in_place/U.S._State_and_Territorial_Stay-At-Home_Orders__March_15__2020___August_15__2021_by_County_by_Day_output.csv diff --git a/dfmdash/cli.py b/dfmdash/cli.py index 3bf360a..9a9e635 100644 --- a/dfmdash/cli.py +++ b/dfmdash/cli.py @@ -75,7 +75,7 @@ def launch(port: str = 8501): Launch Dynamic Factor Dashboard """ current_dir = Path(__file__).resolve().parent - dashboard_path = current_dir / "streamlit" / "Dashboard.py" + dashboard_path = current_dir / "streamlit" / "Dynamic_Factor_Model.py" subprocess.run(["streamlit", "run", dashboard_path, "--server.port", port]) diff --git a/dfmdash/covid19.py b/dfmdash/covid19.py index 5657ea2..6a43b69 100644 --- a/dfmdash/covid19.py +++ b/dfmdash/covid19.py @@ -10,7 +10,7 @@ from dfmdash.constants import NAME_MAP ROOT_DIR = Path(__file__).parent.absolute() -DATA_DIR = ROOT_DIR / "data/processed" +DATA_DIR = ROOT_DIR / "../data/processed" def _get_raw_df() -> pd.DataFrame: diff --git a/dfmdash/data/processed/df_paths.txt b/dfmdash/data/processed/df_paths.txt deleted file mode 100644 index d125448..0000000 --- a/dfmdash/data/processed/df_paths.txt +++ /dev/null @@ -1,33 +0,0 @@ -data/raw/economic/GDP/gdp_current_dollars.csv -data/raw/economic/UI/ar203_output.csv -data/raw/economic/consumption/consumption.csv -data/raw/economic/consumption_durable_goods/consumption_durable_goods.csv -data/raw/economic/consumption_nondurable_goods/consumption_nondurable_goods.csv -data/raw/economic/consumption_services/consumption_services.csv -data/raw/economic/consumption_total_goods/consumption_total_goods.csv -data/raw/economic/employment/CE16OV_output.csv -data/raw/economic/employment/PAYEMS_output.csv -data/raw/economic/inflation/CPIAUCSL_output.csv -data/raw/economic/inflation/PCEPILFE_output.csv -data/raw/economic/inflation/PCEPI_output.csv -data/raw/economic/interest_rates/rates_output.csv -data/raw/economic/investment/K1TTOTL1ES000_output.csv -data/raw/economic/investment/RPFI_output.csv -data/raw/economic/productivity/labor-productivity-by-state-and-region_output.csv -data/raw/economic/unemployment/ststdsadata_output.csv -data/raw/intervention/american_rescue_plan/fiscalrecoveryfunds-statefunding1-CSV_output.csv -data/raw/intervention/cares/cares_state_allocation_output.csv -data/raw/intervention/ppp/PPP_states_and_territories_output.csv -data/raw/pandemic/cases/CDC_state_cases_and_deaths_output.csv -data/raw/pandemic/cases/JHU_time_series_cases_output.csv -data/raw/pandemic/deaths/time_series_covid19_deaths_US_output_FIXED - time_series_covid19_deaths_US_output.csv -data/raw/pandemic/vaccinations/19_Vaccinations_in_the_United_States_Jurisdiction_output_output.csv -data/raw/pandemic/vaccinations/time_series_covid19_vaccine_us_output.csv -data/raw/state_mandates/UWashington/USstatesCov19distancingpolicyBETA_complete_output.csv -data/raw/state_mandates/mask_mandates/U.S._State_and_Territorial_Public_Mask_Mandates_From_April_8__2020_through_August_15__2021_by_State_by_Day_output.csv -data/raw/economic/inflation/pcpiMvMd.csv -data/raw/economic/interest_rates/FEDFUNDS_output.csv -data/raw/economic/inflation/pcpiMvMd.csv -data/raw/economic/interest_rates/FEDFUNDS_output.csv -data/raw/state_mandates/shelter_in_place/U.S._State_and_Territorial_Stay-At-Home_Orders__March_15__2020___August_15__2021_by_County_by_Day_output.csv -data/raw/pandemic/cases/CDC_Case_Surveillance.csv diff --git a/dfmdash/results.py b/dfmdash/results.py index 6c8aecd..2aa8077 100644 --- a/dfmdash/results.py +++ b/dfmdash/results.py @@ -57,7 +57,7 @@ def parse_run_results(directory: Path): df = parse_results(path) # Add the state initials as a column - df["State"] = state_initials + df["Batch"] = state_initials # Append the result to all_results all_results.append(df) diff --git a/dfmdash/streamlit/Dynamic_Factor_Model.py b/dfmdash/streamlit/Dynamic_Factor_Model.py index f3a0467..f41ffc3 100644 --- a/dfmdash/streamlit/Dynamic_Factor_Model.py +++ b/dfmdash/streamlit/Dynamic_Factor_Model.py @@ -47,7 +47,7 @@ def file_uploader(self) -> "DataHandler": st.warning("Please provide input file or check box in sidebar to see Covid-19 Example") st.stop() if load_covid_example: - self.df = pd.read_csv(Path(__file__).parent / "../data/processed/test_input_2state.csv", index_col=0) + self.df = pd.read_csv(Path(__file__).parent / "../../data/processed/test_input_2state.csv", index_col=0) else: self.df = self.load_data(file) c1, _, c2 = st.columns([0.25, 0.05, 0.7]) diff --git a/dfmdash/streamlit/pages/1_Factor_Analysis.py b/dfmdash/streamlit/pages/1_Factor_Analysis.py index 5a4fb2e..252aadc 100644 --- a/dfmdash/streamlit/pages/1_Factor_Analysis.py +++ b/dfmdash/streamlit/pages/1_Factor_Analysis.py @@ -10,7 +10,7 @@ pio.templates.default = "plotly_white" FILE = Path(__file__) -EX_PATH = FILE.parent / "../../data/example-data/pandemic-only" +EX_PATH = FILE.parent / "../../../data/example-data/pandemic-only" def center_title(text): diff --git a/dfmdash/streamlit/pages/2_Comparative_Run_Analysis.py b/dfmdash/streamlit/pages/2_Comparative_Run_Analysis.py index 7fab578..9ec2452 100644 --- a/dfmdash/streamlit/pages/2_Comparative_Run_Analysis.py +++ b/dfmdash/streamlit/pages/2_Comparative_Run_Analysis.py @@ -23,20 +23,22 @@ def center_title(text): center_title("Comparative Run Analysis") # Parameter to runs -run_dir = Path(st.text_input("Path directory of runs", value="./dfmdash/data/example-data")) +FILE_PATH = Path(__file__).parent +EXAMPLE_RESULT_DIR = FILE_PATH / "../../../data/example-data" +run_dir = Path(st.text_input("Path directory of runs", value=EXAMPLE_RESULT_DIR)) df = parse_multiple_runs(run_dir).sort_values("Run") def create_plot(df): # Create Streamlit expander for user inputs with st.expander("Filter options"): - states = st.multiselect("Select States", df["State"].unique(), default=df["State"].unique()) + states = st.multiselect("Select Batchs", df["Batch"].unique(), default=df["Batch"].unique()) metric = st.sidebar.selectbox("Select Metric", df.columns[:3]) nbins = st.sidebar.slider("nbins", min_value=10, max_value=500, value=50) log_x = st.sidebar.checkbox("Log X-axis") # Filter DataFrame based on user inputs - df_filtered = df[df["State"].isin(states)] + df_filtered = df[df["Batch"].isin(states)] # Create Plotly figure fig = px.histogram( @@ -45,7 +47,7 @@ def create_plot(df): color="Run", marginal="box", nbins=nbins, - hover_data=["State"], + hover_data=["Batch"], log_x=log_x, opacity=0.5, barmode="overlay", @@ -75,7 +77,7 @@ def get_summary(df: pd.DataFrame): # Median metrics run_name = df.Run.iloc[0] col1, col2, col3, col4 = st.columns(4) - col1.metric("Number of Failed States", num_failures(run_dir, run_name), delta_failures(run_dir, run_name)) + col1.metric("Number of Failed Batchs", num_failures(run_dir, run_name), delta_failures(run_dir, run_name)) col2.metric("Median Log Likelihood", df["Log Likelihood"].median()) col3.metric("Median AIC", df["AIC"].median()) col4.metric("Median EM Iterations", df["EM Iterations"].median()) diff --git a/dfmdash/streamlit/pages/4_Synthetic_Control_Model.py b/dfmdash/streamlit/pages/4_Synthetic_Control_Model.py index ba5cbe1..adb14fe 100644 --- a/dfmdash/streamlit/pages/4_Synthetic_Control_Model.py +++ b/dfmdash/streamlit/pages/4_Synthetic_Control_Model.py @@ -1,24 +1,16 @@ from pathlib import Path -import matplotlib.pyplot as plt -import seaborn as sns -import plotly.express as px -import anndata as ann import numpy as np import pandas as pd import plotly.graph_objects as go import plotly.io as pio import streamlit as st -from SyntheticControlMethods import DiffSynth, Synth -from datetime import datetime - -from dfmdash.constants import FACTORS_GROUPED -from dfmdash.covid19 import get_df, get_project_h5ad +from SyntheticControlMethods import Synth st.set_page_config(layout="wide") pio.templates.default = "plotly_dark" -EX_PATH = Path("./dfmdash/data/example-data/pandemic-only") +EX_PATH = Path(__file__).parent / "../../../data/example-data/pandemic-only" def center_title(text): diff --git a/poetry.lock b/poetry.lock index bc161df..706a5d8 100644 --- a/poetry.lock +++ b/poetry.lock @@ -3466,14 +3466,15 @@ watchdog = {version = ">=2.1.5", markers = "platform_system != \"Darwin\""} snowflake = ["snowflake-connector-python (>=2.8.0)", "snowflake-snowpark-python (>=0.9.0)"] [[package]] -name = "SyntheticControlMethods" +name = "syntheticcontrolmethods" version = "1.1.17" description = "A Python package for causal inference using various Synthetic Control Methods" category = "main" optional = false python-versions = "*" -files = [] -develop = false +files = [ + {file = "SyntheticControlMethods-1.1.17.tar.gz", hash = "sha256:941c77427ef1a87d5cf5863047c5d0c77c280a6b3aea04de3302e56fa76fe505"}, +] [package.dependencies] cvxpy = ">=1.1.7" @@ -3483,12 +3484,6 @@ numpy = ">=1.17" pandas = ">=1.1.2" scipy = ">=1.4.1" -[package.source] -type = "git" -url = "https://github.com/jvivian/SyntheticControlMethods" -reference = "master" -resolved_reference = "3f496b36ed46c4e5c1e08ce6e903013e6eeb29df" - [[package]] name = "tenacity" version = "8.2.3" @@ -3849,4 +3844,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.9.7 || >3.9.7,<3.12" -content-hash = "7a36a6a0d662b8e01066a795f9d2b7bb3e8f4dfd65ed004d58b37e4f4f654f22" +content-hash = "a34066e9b6a9ff6b86ee9adde5d7ec4374292b496e4a55376b7e469ef87b970d" diff --git a/pyproject.toml b/pyproject.toml index 4231c0b..e643322 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "dfmdash" -version = "0.0.1" +version = "1.0.0" description = "Repository for Covid-19 Dynamic Factor Model" authors = ["J. Vivian, A. Cooke, J. Fitz "] repository = "https://github.com/jvivian/DFMDash" @@ -35,7 +35,7 @@ urllib3 = ">=1.26.16,<2.0.0" decorator = "^5.1.1" anndata = "^0.10.6" arviz = "^0.17.1" -syntheticcontrolmethods = {git = "https://github.com/jvivian/SyntheticControlMethods", branch="master"} +syntheticcontrolmethods = "^1.1.17" [tool.poetry.group.dev.dependencies] pytest = "^7.2.0" diff --git a/reports/figures/Final.csv b/reports/figures/Final.csv new file mode 100644 index 0000000..fd58185 --- /dev/null +++ b/reports/figures/Final.csv @@ -0,0 +1,1453 @@ +Time,State,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +1/1/2012,CT,238073.021,15285.2394,31050.91469,105935.1728,46336.1541,0,0,0,0,0,,,,,,, +2/1/2012,CT,238702.9317,15278.43354,31023.35627,105961.8096,46301.78981,0,0,0,0,0,,,,,,, +3/1/2012,CT,239406.8281,15276.55788,31005.86554,106022.3342,46282.42342,0,0,0,0,0,,,,,,, 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b/reports/figures/figures.ipynb @@ -0,0 +1,1924 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from SyntheticControlMethods import DiffSynth, Synth\n", + "\n", + "from dfmdash.dfm import ModelRunner\n", + "from dfmdash.results import parse_run_results\n", + "\n", + "plt.style.use(\"ggplot\")\n", + "\n", + "FIG_OUT = Path(\"Paper_Figures\")\n", + "FIG_OUT.mkdir(exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Read in Factors" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Time Factor_Pandemic Factor_Consumption Factor_GDP Factor_Cons3 \\\n", + "0 2/1/2012 -1.161055 0.170095 294888.4089 22717.46446 \n", + "1 3/1/2012 -1.171222 -0.025467 295976.4376 22726.87622 \n", + "2 4/1/2012 -1.171824 -0.416690 296195.7022 22749.68458 \n", + "3 5/1/2012 -1.171860 -2.142386 297136.4557 22827.90665 \n", + "4 6/1/2012 -1.171862 -1.655460 297893.9661 22892.06050 \n", + "\n", + " Factor_Cons4 Factor_Cons5 Factor_Cons2 Factor_Cases5 Factor_Cases2 \\\n", + "0 43092.70514 135937.3315 65810.18635 0.0 0.0 \n", + "1 43088.33409 136033.2130 65815.23537 0.0 0.0 \n", + "2 43109.43331 136209.1536 65859.15126 0.0 0.0 \n", + "3 43235.54277 136716.8652 66063.49119 0.0 0.0 \n", + "4 43334.97152 137140.3855 66227.08216 0.0 0.0 \n", + "\n", + " ... Factor_Deaths2 Factor_Cases3 Factor_Deaths3 Factor_Cases4 \\\n", + "0 ... 0.0 0.0 NaN NaN \n", + "1 ... 0.0 0.0 NaN NaN \n", + "2 ... 0.0 0.0 NaN NaN \n", + "3 ... 0.0 0.0 NaN NaN \n", + "4 ... 0.0 0.0 NaN NaN \n", + "\n", + " Factor_Cases1 Factor_Hosp2 Factor_Hosp1 Factor_Deaths4 Factor_Deaths1 \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " State \n", + "0 MN \n", + "1 MN \n", + "2 MN \n", + "3 MN \n", + "4 MN \n", + "\n", + "[5 rows x 21 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_factors = pd.read_csv('./results/factors.csv')\n", + "all_factors.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def normalize(df, batch_col=None):\n", + " time = df.index\n", + " if batch_col:\n", + " batch_column = df[batch_col].copy()\n", + " df = df.drop(columns=[batch_col])\n", + " df = pd.DataFrame(MinMaxScaler().fit_transform(df), columns=df.columns, index=time)\n", + " df[batch_col] = batch_column\n", + " else:\n", + " df = pd.DataFrame(MinMaxScaler().fit_transform(df), columns=df.columns, index=time)\n", + " return df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Read in normalized data" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TimeGDPCons3Cons4Cons5Cons2Cases5Cases2Deaths5Deaths2Cases3Deaths3Cases4Cases1Hosp2Hosp1Deaths4Deaths1State
02/1/20120.6530340.3435260.2856760.5999390.2372970.00.00.00.00.00.00.00.00.00.00.00.0MN
13/1/20120.6585310.3647400.3091200.6167110.2661720.00.00.00.00.00.00.00.00.00.00.00.0MN
24/1/20120.6035920.4037340.3520810.6476030.3191360.00.00.00.00.00.00.00.00.00.00.00.0MN
35/1/20120.6489750.5646030.5288320.7752850.5372310.00.00.00.00.00.00.00.00.00.00.00.0MN
46/1/20120.6373060.5231440.4834720.7422860.4811860.00.00.00.00.00.00.00.00.00.00.00.0MN
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" + ], + "text/plain": [ + " Time GDP Cons3 Cons4 Cons5 Cons2 Cases5 Cases2 \\\n", + "0 2/1/2012 0.653034 0.343526 0.285676 0.599939 0.237297 0.0 0.0 \n", + "1 3/1/2012 0.658531 0.364740 0.309120 0.616711 0.266172 0.0 0.0 \n", + "2 4/1/2012 0.603592 0.403734 0.352081 0.647603 0.319136 0.0 0.0 \n", + "3 5/1/2012 0.648975 0.564603 0.528832 0.775285 0.537231 0.0 0.0 \n", + "4 6/1/2012 0.637306 0.523144 0.483472 0.742286 0.481186 0.0 0.0 \n", + "\n", + " Deaths5 Deaths2 Cases3 Deaths3 Cases4 Cases1 Hosp2 Hosp1 Deaths4 \\\n", + "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " Deaths1 State \n", + "0 0.0 MN \n", + "1 0.0 MN \n", + "2 0.0 MN \n", + "3 0.0 MN \n", + "4 0.0 MN " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dfs = []\n", + "for subdir in Path('./results').iterdir():\n", + " if subdir.is_dir():\n", + " raw = pd.read_csv(subdir / 'df.csv')\n", + " raw['State'] = subdir.name\n", + " dfs.append(raw)\n", + "\n", + "norm = pd.concat(dfs)\n", + "norm.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Figure 1 - Pandemic" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TimeMetricNormalized ValueLabel
2452020-02-01Cases10.000000Data
2462020-03-01Cases10.033052Data
2472020-04-01Cases10.138976Data
2482020-05-01Cases10.072053Data
2492020-06-01Cases10.016776Data
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" + ], + "text/plain": [ + " Time Metric Normalized Value Label\n", + "245 2020-02-01 Cases1 0.000000 Data\n", + "246 2020-03-01 Cases1 0.033052 Data\n", + "247 2020-04-01 Cases1 0.138976 Data\n", + "248 2020-05-01 Cases1 0.072053 Data\n", + "249 2020-06-01 Cases1 0.016776 Data" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "columns = [\n", + " \"Cases5\",\n", + " \"Cases2\",\n", + " \"Deaths5\",\n", + " \"Deaths2\",\n", + " \"Cases3\",\n", + " \"Deaths3\",\n", + " \"Cases4\",\n", + " \"Cases1\",\n", + " \"Hosp1\",\n", + " \"Deaths4\",\n", + " \"Deaths1\",\n", + "]\n", + "factor = \"Factor_Pandemic\"\n", + "state = \"MA\"\n", + "time = datetime(2020, 1, 1)\n", + "invert = True\n", + "df = norm[norm.State == state].set_index(\"Time\").drop(columns=[\"State\"])[columns]\n", + "factors = all_factors[all_factors.State == state].set_index(\"Time\")\n", + "\n", + "if invert:\n", + " factors[factor] = factors[factor] * -1\n", + "\n", + "df = df.join(factors[[factor]]).rename(columns={factor: factor[7:]})\n", + "df.index = pd.DatetimeIndex(df.index)\n", + "df = df[df.index > time]\n", + "df = normalize(df)\n", + "df = df.melt(var_name=\"Metric\", value_name=\"Normalized Value\", ignore_index=False)\n", + "df['Label'] = ['Factor' if x == factor[7:] else 'Data' for x in df.Metric]\n", + "df = df.reset_index().sort_values(['Metric', 'Time'])\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Theme 1" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams.update(\n", + " {\n", + " \"font.family\": \"serif\",\n", + " \"font.size\": 14,\n", + " \"axes.labelsize\": 14,\n", + " \"axes.titlesize\": 20,\n", + " \"legend.fontsize\": 14,\n", + " \"xtick.labelsize\": 12,\n", + " \"ytick.labelsize\": 12,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use(\"ggplot\")\n", + "fig, ax = plt.subplots(figsize=(12, 4))\n", + "\n", + "# Plot the raw data with subtle colors\n", + "for metric, group in df[df['Label'] == 'Data'].reset_index().groupby('Metric'):\n", + " ax.plot(group['Time'], group['Normalized Value'], color='gray', alpha=0.5, linewidth=0.8, label='_nolegend_')\n", + "\n", + "# Plot the factor with a contrasting color and thicker line\n", + "factor_data = df[df['Label'] == 'Factor'].reset_index()\n", + "ax.plot(factor_data['Time'], factor_data['Normalized Value'], color='black', linewidth=2, label='Pandemic Factor')\n", + "\n", + "# Customize the plot appearance\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['left'].set_position(('outward', 10))\n", + "ax.spines['bottom'].set_position(('outward', 10))\n", + "ax.tick_params(direction='out', length=6, width=0.8)\n", + "ax.tick_params(axis='y', which='both', left=True, right=False)\n", + "ax.tick_params(axis='x', which='both', bottom=True, top=False)\n", + "ax.set_xlabel('Time', fontsize=16)\n", + "ax.set_ylabel('Normalized Value', fontsize=16)\n", + "\n", + "# Minimalist legend placement\n", + "ax.legend(frameon=False, loc='upper right')\n", + "\n", + "# Add labels\n", + "ax.set_xlabel('Time')\n", + "ax.set_ylabel('Normalized Value')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Theme 2" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set_style('white')\n", + "fig, ax = plt.subplots(figsize=(12, 4))\n", + "\n", + "# Plot the raw data with subtle colors\n", + "for metric, group in df[df['Label'] == 'Data'].reset_index().groupby('Metric'):\n", + " ax.plot(group['Time'], group['Normalized Value'], color='gray', alpha=0.5, linewidth=0.8, label='_nolegend_')\n", + "\n", + "# Plot the factor with a contrasting color and thicker line\n", + "factor_data = df[df['Label'] == 'Factor'].reset_index()\n", + "ax.plot(factor_data['Time'], factor_data['Normalized Value'], color='black', linewidth=2, label='Pandemic Factor')\n", + "\n", + "# Customize the plot appearance\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['left'].set_position(('outward', 10))\n", + "ax.spines['bottom'].set_position(('outward', 10))\n", + "ax.tick_params(direction='out', length=6, width=0.8)\n", + "ax.tick_params(axis='y', which='both', left=True, right=False)\n", + "ax.tick_params(axis='x', which='both', bottom=True, top=False)\n", + "ax.set_xlabel('Time', fontsize=16)\n", + "ax.set_ylabel('Normalized Value', fontsize=16)\n", + "\n", + "# Minimalist legend placement\n", + "ax.legend(frameon=False, loc='upper right')\n", + "\n", + "# Add labels\n", + "ax.set_xlabel('Time')\n", + "ax.set_ylabel('Normalized Value')\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(FIG_OUT / \"MA-Pandemic.png\", dpi=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Figure 2 - Consumption" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TimeMetricNormalized ValueLabel
352020-02-01Cons20.339886Data
362020-03-01Cons20.595546Data
372020-04-01Cons20.831451Data
382020-05-01Cons20.345702Data
392020-06-01Cons20.154480Data
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" + ], + "text/plain": [ + " Time Metric Normalized Value Label\n", + "35 2020-02-01 Cons2 0.339886 Data\n", + "36 2020-03-01 Cons2 0.595546 Data\n", + "37 2020-04-01 Cons2 0.831451 Data\n", + "38 2020-05-01 Cons2 0.345702 Data\n", + "39 2020-06-01 Cons2 0.154480 Data" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "columns = [\n", + " \"GDP\",\n", + " \"Cons2\",\n", + " \"Cons3\",\n", + " \"Cons4\",\n", + " \"Cons5\",\n", + "]\n", + "factor = \"Factor_Consumption\"\n", + "state = \"MA\"\n", + "time = datetime(2020, 1, 1)\n", + "invert = True\n", + "df = norm[norm.State == state].set_index(\"Time\").drop(columns=[\"State\"])[columns]\n", + "factors = all_factors[all_factors.State == state].set_index(\"Time\")\n", + "\n", + "if invert:\n", + " factors[factor] = factors[factor] * -1\n", + "\n", + "df = df.join(factors[[factor]]).rename(columns={factor: factor[7:]})\n", + "df.index = pd.DatetimeIndex(df.index)\n", + "df = df[df.index > time]\n", + "df = normalize(df)\n", + "df = df.melt(var_name=\"Metric\", value_name=\"Normalized Value\", ignore_index=False)\n", + "df['Label'] = ['Factor' if x == factor[7:] else 'Data' for x in df.Metric]\n", + "df = df.reset_index().sort_values(['Metric', 'Time'])\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Theme 1" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use(\"ggplot\")\n", + "fig, ax = plt.subplots(figsize=(12, 4))\n", + "\n", + "# Plot the raw data with subtle colors\n", + "for metric, group in df[df['Label'] == 'Data'].reset_index().groupby('Metric'):\n", + " ax.plot(group['Time'], group['Normalized Value'], color='gray', alpha=0.5, linewidth=0.8, label='_nolegend_')\n", + "\n", + "# Plot the factor with a contrasting color and thicker line\n", + "factor_data = df[df['Label'] == 'Factor'].reset_index()\n", + "ax.plot(factor_data['Time'], factor_data['Normalized Value'], color='black', linewidth=2, label='Economic Factor')\n", + "\n", + "# Customize the plot appearance\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['left'].set_position(('outward', 10))\n", + "ax.spines['bottom'].set_position(('outward', 10))\n", + "ax.tick_params(direction='out', length=6, width=0.8)\n", + "ax.tick_params(axis='y', which='both', left=True, right=False)\n", + "ax.tick_params(axis='x', which='both', bottom=True, top=False)\n", + "ax.set_xlabel('Time', fontsize=16)\n", + "ax.set_ylabel('Normalized Value', fontsize=16)\n", + "\n", + "# Minimalist legend placement\n", + "ax.legend(frameon=False, loc='upper right')\n", + "\n", + "# Add labels\n", + "ax.set_xlabel('Time')\n", + "ax.set_ylabel('Normalized Value')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Theme 2" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set_style('white')\n", + "fig, ax = plt.subplots(figsize=(12, 4))\n", + "\n", + "# Plot the raw data with subtle colors\n", + "for metric, group in df[df['Label'] == 'Data'].reset_index().groupby('Metric'):\n", + " ax.plot(group['Time'], group['Normalized Value'], color='gray', alpha=0.5, linewidth=0.8, label='_nolegend_')\n", + "\n", + "# Plot the factor with a contrasting color and thicker line\n", + "factor_data = df[df['Label'] == 'Factor'].reset_index()\n", + "ax.plot(factor_data['Time'], factor_data['Normalized Value'], color='black', linewidth=2, label='Economic Factor')\n", + "\n", + "# Customize the plot appearance\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['left'].set_position(('outward', 10))\n", + "ax.spines['bottom'].set_position(('outward', 10))\n", + "ax.tick_params(direction='out', length=6, width=0.8)\n", + "ax.tick_params(axis='y', which='both', left=True, right=False)\n", + "ax.tick_params(axis='x', which='both', bottom=True, top=False)\n", + "ax.set_xlabel('Time', fontsize=16)\n", + "ax.set_ylabel('Normalized Value', fontsize=16)\n", + "\n", + "# Minimalist legend placement\n", + "ax.legend(frameon=False, loc='upper right')\n", + "\n", + "# Add labels\n", + "ax.set_xlabel('Time')\n", + "ax.set_ylabel('Normalized Value')\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(FIG_OUT / \"MA-Economic.png\", dpi=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SCM Figure" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GDPCons3Cons4Cons5Cons2Cases5Cases2Deaths5Deaths2Cases3Deaths3Cases4Cases1Hosp2Hosp1Deaths4Deaths1PandemicConsumption
TimeState
2/1/2012MN0.6530340.3435260.2856760.5999390.2372970.00.00.00.00.00.00.00.00.00.00.00.0-1.1610550.170095
3/1/2012MN0.6585310.3647400.3091200.6167110.2661720.00.00.00.00.00.00.00.00.00.00.00.0-1.171222-0.025467
4/1/2012MN0.6035920.4037340.3520810.6476030.3191360.00.00.00.00.00.00.00.00.00.00.00.0-1.171824-0.416690
5/1/2012MN0.6489750.5646030.5288320.7752850.5372310.00.00.00.00.00.00.00.00.00.00.00.0-1.171860-2.142386
6/1/2012MN0.6373060.5231440.4834720.7422860.4811860.00.00.00.00.00.00.00.00.00.00.00.0-1.171862-1.655460
\n", + "
" + ], + "text/plain": [ + " GDP Cons3 Cons4 Cons5 Cons2 Cases5 \\\n", + "Time State \n", + "2/1/2012 MN 0.653034 0.343526 0.285676 0.599939 0.237297 0.0 \n", + "3/1/2012 MN 0.658531 0.364740 0.309120 0.616711 0.266172 0.0 \n", + "4/1/2012 MN 0.603592 0.403734 0.352081 0.647603 0.319136 0.0 \n", + "5/1/2012 MN 0.648975 0.564603 0.528832 0.775285 0.537231 0.0 \n", + "6/1/2012 MN 0.637306 0.523144 0.483472 0.742286 0.481186 0.0 \n", + "\n", + " Cases2 Deaths5 Deaths2 Cases3 Deaths3 Cases4 Cases1 \\\n", + "Time State \n", + "2/1/2012 MN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "3/1/2012 MN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "4/1/2012 MN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "5/1/2012 MN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "6/1/2012 MN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " Hosp2 Hosp1 Deaths4 Deaths1 Pandemic Consumption \n", + "Time State \n", + "2/1/2012 MN 0.0 0.0 0.0 0.0 -1.161055 0.170095 \n", + "3/1/2012 MN 0.0 0.0 0.0 0.0 -1.171222 -0.025467 \n", + "4/1/2012 MN 0.0 0.0 0.0 0.0 -1.171824 -0.416690 \n", + "5/1/2012 MN 0.0 0.0 0.0 0.0 -1.171860 -2.142386 \n", + "6/1/2012 MN 0.0 0.0 0.0 0.0 -1.171862 -1.655460 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_dir = Path(\"./results\")\n", + "factors_path = result_dir / \"factors.csv\"\n", + "\n", + "fdf = pd.read_csv(factors_path)\n", + "cols_to_drop = [x for x in fdf.columns if \"Time.\" in x]\n", + "fdf = fdf.drop(columns=cols_to_drop)\n", + "fdf.columns = [x.lstrip(\"Factor_\") for x in fdf.columns]\n", + "\n", + "# Process Data\n", + "dfs = []\n", + "for subdir in result_dir.iterdir():\n", + " if not subdir.is_dir():\n", + " continue\n", + " state = pd.read_csv(subdir / \"df.csv\")\n", + " state[\"State\"] = subdir.stem\n", + " dfs.append(state)\n", + "df = pd.concat(dfs)\n", + "df = df.set_index([\"Time\", \"State\"])\n", + "columns = [x for x in fdf.columns if x not in df.columns]\n", + "min_time = pd.to_datetime(fdf[\"Time\"]).min()\n", + "max_time = pd.to_datetime(fdf[\"Time\"].max())\n", + "states = sorted(fdf.State.unique())\n", + "fdf = fdf[columns].set_index([\"Time\", \"State\"])\n", + "df = df.join(fdf)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RUNNING THE L-BFGS-B CODE\n", + "\n", + " * * *\n", + "\n", + "Machine precision = 2.220D-16\n", + " N = 3 M = 10\n", + "\n", + "At X0 1 variables are exactly at the bounds\n", + "\n", + "At iterate 0 f= 4.12456D+01 |proj g|= 5.00000D-01\n", + "\n", + " * * *\n", + "\n", + "Tit = total number of iterations\n", + "Tnf = total number of function evaluations\n", + "Tnint = total number of segments explored during Cauchy searches\n", + "Skip = number of BFGS updates skipped\n", + "Nact = number of active bounds at final generalized Cauchy point\n", + "Projg = norm of the final projected gradient\n", + "F = final function value\n", + "\n", + " * * *\n", + "\n", + " N Tit Tnf Tnint Skip Nact Projg F\n", + " 3 1 17 3 0 3 5.000D-01 4.125D+01\n", + " F = 41.245615368651137 \n", + "\n", + "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH \n", + "RUNNING THE L-BFGS-B CODE\n", + "\n", + " * * *\n", + "\n", + "Machine precision = 2.220D-16\n", + " N = 3 M = 10\n", + "\n", + "At X0 0 variables are exactly at the bounds\n", + "\n", + "At iterate 0 f= 4.36631D+02 |proj g|= 0.00000D+00\n", + "\n", + " * * *\n", + "\n", + "Tit = total number of iterations\n", + "Tnf = total number of function evaluations\n", + "Tnint = total number of segments explored during Cauchy searches\n", + "Skip = number of BFGS updates skipped\n", + "Nact = number of active bounds at final generalized Cauchy point\n", + "Projg = norm of the final projected gradient\n", + "F = final function value\n", + "\n", + " * * *\n", + "\n", + " N Tit Tnf Tnint Skip Nact Projg F\n", + " 3 0 1 0 0 0 0.000D+00 4.366D+02\n", + " F = 436.63099371809011 \n", + "\n", + "CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL \n", + "RUNNING THE L-BFGS-B CODE\n", + "\n", + " * * *\n", + "\n", + "Machine precision = 2.220D-16\n", + " N = 3 M = 10\n", + "\n", + "At X0 0 variables are exactly at the bounds\n", + "\n", + "At iterate 0 f= 4.36631D+02 |proj g|= 0.00000D+00\n", + "\n", + " * * *\n", + "\n", + "Tit = total number of iterations\n", + "Tnf = total number of function evaluations\n", + "Tnint = total number of segments explored during Cauchy searches\n", + "Skip = number of BFGS updates skipped\n", + "Nact = number of active bounds at final generalized Cauchy point\n", + "Projg = norm of the final projected gradient\n", + "F = final function value\n", + "\n", + " * * *\n", + "\n", + " N Tit Tnf Tnint Skip Nact Projg F\n", + " 3 0 1 0 0 0 0.000D+00 4.366D+02\n", + " F = 436.63099371809011 \n", + "\n", + "CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL \n", + "RUNNING THE L-BFGS-B CODE\n", + "\n", + " * * *\n", + "\n", + "Machine precision = 2.220D-16\n", + " N = 3 M = 10\n", + "\n", + "At X0 0 variables are exactly at the bounds\n", + "\n", + "At iterate 0 f= 4.36631D+02 |proj g|= 0.00000D+00\n", + "\n", + " * * *\n", + "\n", + "Tit = total number of iterations\n", + "Tnf = total number of function evaluations\n", + "Tnint = total number of segments explored during Cauchy searches\n", + "Skip = number of BFGS updates skipped\n", + "Nact = number of active bounds at final generalized Cauchy point\n", + "Projg = norm of the final projected gradient\n", + "F = final function value\n", + "\n", + " * * *\n", + "\n", + " N Tit Tnf Tnint Skip Nact Projg F\n", + " 3 0 1 0 0 0 0.000D+00 4.366D+02\n", + " F = 436.63099371809011 \n", + "\n", + "CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL \n", + "RUNNING THE L-BFGS-B CODE\n", + "\n", + " * * *\n", + "\n", + "Machine precision = 2.220D-16\n", + " N = 3 M = 10\n", + "\n", + "At X0 0 variables are exactly at the bounds\n", + "\n", + "At iterate 0 f= 4.36631D+02 |proj g|= 0.00000D+00\n", + "\n", + " * * *\n", + "\n", + "Tit = total number of iterations\n", + "Tnf = total number of function evaluations\n", + "Tnint = total number of segments explored during Cauchy searches\n", + "Skip = number of BFGS updates skipped\n", + "Nact = number of active bounds at final generalized Cauchy point\n", + "Projg = norm of the final projected gradient\n", + "F = final function value\n", + "\n", + " * * *\n", + "\n", + " N Tit Tnf Tnint Skip Nact Projg F\n", + " 3 0 1 0 0 0 0.000D+00 4.366D+02\n", + " F = 436.63099371809011 \n", + "\n", + "CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL \n", + "RUNNING THE L-BFGS-B CODE\n", + "\n", + " * * *\n", + "\n", + "Machine precision = 2.220D-16\n", + " N = 3 M = 10\n", + "\n", + "At X0 0 variables are exactly at the bounds\n", + "\n", + "At iterate 0 f= 6.67586D+01 |proj g|= 0.00000D+00\n", + "\n", + " * * *\n", + "\n", + "Tit = total number of iterations\n", + "Tnf = total number of function evaluations\n", + "Tnint = total number of segments explored during Cauchy searches\n", + "Skip = number of BFGS updates skipped\n", + "Nact = number of active bounds at final generalized Cauchy point\n", + "Projg = norm of the final projected gradient\n", + "F = final function value\n", + "\n", + " * * *\n", + "\n", + " N Tit Tnf Tnint Skip Nact Projg F\n", + " 3 0 1 0 0 0 0.000D+00 6.676D+01\n", + " F = 66.758611291962936 \n", + "\n", + "CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL \n", + "RUNNING THE L-BFGS-B CODE\n", + "\n", + " * * *\n", + "\n", + "Machine precision = 2.220D-16\n", + " N = 3 M = 10\n", + "\n", + "At X0 0 variables are exactly at the bounds\n", + "\n", + "At iterate 0 f= 4.36631D+02 |proj g|= 0.00000D+00\n", + "\n", + " * * *\n", + "\n", + "Tit = total number of iterations\n", + "Tnf = total number of function evaluations\n", + "Tnint = total number of segments explored during Cauchy searches\n", + "Skip = number of BFGS updates skipped\n", + "Nact = number of active bounds at final generalized Cauchy point\n", + "Projg = norm of the final projected gradient\n", + "F = final function value\n", + "\n", + " * * *\n", + "\n", + " N Tit Tnf Tnint Skip Nact Projg F\n", + " 3 0 1 0 0 0 0.000D+00 4.366D+02\n", + " F = 436.63099371809011 \n", + "\n", + "CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL \n", + "RUNNING THE L-BFGS-B CODE\n", + "\n", + " * * *\n", + "\n", + "Machine precision = 2.220D-16\n", + " N = 3 M = 10\n", + "\n", + "At X0 0 variables are exactly at the bounds\n", + "\n", + "At iterate 0 f= 4.36631D+02 |proj g|= 0.00000D+00\n", + "\n", + " * * *\n", + "\n", + "Tit = total number of iterations\n", + "Tnf = total number of function evaluations\n", + "Tnint = total number of segments explored during Cauchy searches\n", + "Skip = number of BFGS updates skipped\n", + "Nact = number of active bounds at final generalized Cauchy point\n", + "Projg = norm of the final projected gradient\n", + "F = final function value\n", + "\n", + " * * *\n", + "\n", + " N Tit Tnf Tnint Skip Nact Projg F\n", + " 3 0 1 0 0 0 0.000D+00 4.366D+02\n", + " F = 436.63099371809011 \n", + "\n", + "CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL \n", + "RUNNING THE L-BFGS-B CODE\n", + "\n", + " * * *\n", + "\n", + "Machine precision = 2.220D-16\n", + " N = 3 M = 10\n", + "\n", + "At X0 0 variables are exactly at the bounds\n", + "\n", + "At iterate 0 f= 4.36631D+02 |proj g|= 0.00000D+00\n", + "\n", + " * * *\n", + "\n", + "Tit = total number of iterations\n", + "Tnf = total number of function evaluations\n", + "Tnint = total number of segments explored during Cauchy searches\n", + "Skip = number of BFGS updates skipped\n", + "Nact = number of active bounds at final generalized Cauchy point\n", + "Projg = norm of the final projected gradient\n", + "F = final function value\n", + "\n", + " * * *\n", + "\n", + " N Tit Tnf Tnint Skip Nact Projg F\n", + " 3 0 1 0 0 0 0.000D+00 4.366D+02\n", + " F = 436.63099371809011 \n", + "\n", + "CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL \n", + "RUNNING THE L-BFGS-B CODE\n", + "\n", + " * * *\n", + "\n", + "Machine precision = 2.220D-16\n", + " N = 3 M = 10\n", + "\n", + "At X0 0 variables are exactly at the bounds\n", + "\n", + "At iterate 0 f= 1.14206D+02 |proj g|= 2.13081D+00\n", + " ys=-1.418E+10 -gs= 3.667E+02 BFGS update SKIPPED\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + " Warning: more than 10 function and gradient\n", + " evaluations in the last line search. Termination\n", + " may possibly be caused by a bad search direction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "At iterate 3 f= 4.12456D+01 |proj g|= 5.56677D-06\n", + "\n", + " * * *\n", + "\n", + "Tit = total number of iterations\n", + "Tnf = total number of function evaluations\n", + "Tnint = total number of segments explored during Cauchy searches\n", + "Skip = number of BFGS updates skipped\n", + "Nact = number of active bounds at final generalized Cauchy point\n", + "Projg = norm of the final projected gradient\n", + "F = final function value\n", + "\n", + " * * *\n", + "\n", + " N Tit Tnf Tnint Skip Nact Projg F\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + " Bad direction in the line search;\n", + " refresh the lbfgs memory and restart the iteration.\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Weight\n", + "MN 0.058439\n", + "IL 0.098993\n", + "VA 0.161583\n", + "RI 0.102443\n", + "CT 0.214926\n", + "PA 0.057350\n", + "NJ 0.078189\n", + "NY 0.108720\n", + "MI 0.058789\n", + "ME 0.060567" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 3 4 40 10 1 3 1.000D+00 4.125D+01\n", + " F = 41.245615251205507 \n", + "\n", + "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH \n" + ] + } + ], + "source": [ + "# Select Variables\n", + "treated_unit = 'MA'\n", + "predictor_vars = ['Consumption']\n", + "outcome_var = 'Pandemic'\n", + "treatment_time = datetime(2022, 2, 1)\n", + "\n", + "invert = True\n", + "\n", + "\n", + "df = df[[*predictor_vars, outcome_var]].reset_index()\n", + "sc = Synth(df, outcome_var, \"State\", \"Time\", str(treatment_time), treated_unit, n_optim=10, pen=\"auto\")\n", + "\n", + "\n", + "sc.original_data.weight_df" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MASynthetic MAWMAPEImportance
Consumption-0.15-0.140.101.0
Pandemic-0.23-0.100.310.0
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" + ], + "text/plain": [ + " MA Synthetic MA WMAPE Importance\n", + "Consumption -0.15 -0.14 0.10 1.0\n", + "Pandemic -0.23 -0.10 0.31 0.0" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sc.original_data.comparison_df" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "data = sc.original_data\n", + "synth = data.synth_outcome\n", + "treated_outcome_all = data.treated_outcome_all\n", + "treatment_period = data.treatment_period\n", + "treated_label = \"Treated\"\n", + "synth_label = \"Synthetic Control\"\n", + "treatment_label = \"Treatment\"\n", + "\n", + "# Determine appropriate limits for y-axis\n", + "max_value = max(np.max(treated_outcome_all), np.max(synth))\n", + "min_value = min(np.min(treated_outcome_all), np.min(synth))\n", + "\n", + "# Create x/y\n", + "x = df[df.State == treated_unit].Time\n", + "ys = synth[0, :]\n", + "y = df[df.State == treated_unit][outcome_var]\n", + "\n", + "if invert:\n", + " y *= -1\n", + " ys *= -1\n", + "\n", + "# fig.add_trace(go.Scatter(x=x, y=y, mode=\"lines\", name=f\"{treated_unit} {outcome_var}\"))\n", + "# fig.add_trace(go.Scatter(x=x, y=ys, mode=\"lines\", name=f\"Synthetic {outcome_var}\", line=dict(dash=\"dot\")))" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp('2022-12-01 00:00:00')" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = pd.to_datetime(x)\n", + "x.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Original" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 6))\n", + "sns.lineplot(x=x, y=y, label=f\"{treated_unit} {outcome_var}\", c='black')\n", + "sns.lineplot(x=x, y=ys, label=f\"Synthetic {outcome_var}\", c='grey', linestyle='--')\n", + "plt.xlim([datetime(2022, 1,1), x.max()])\n", + "plt.ylim([-1, 12])\n", + "plt.savefig(FIG_OUT / \"MA-Pandemic-Synth.png\", dpi=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pointwise" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "synth_delta = synth[0, :] - treated_outcome_all[0, :]\n", + "flat_y = np.zeros_like(synth_delta) # Pandemic as a flat line (zero)\n", + "\n", + "# Plotting\n", + "plt.figure(figsize=(12, 6))\n", + "sns.lineplot(x=x, y=flat_y, label=\"Pandemic Baseline\", color='black') \n", + "sns.lineplot(x=x, y=ys - y, label=\"Synthetic Deviation\", color='grey', linestyle='--')\n", + "\n", + "# Mark the treatment period\n", + "plt.axvline(x=pd.to_datetime(treatment_period), color='red', linestyle=':', label=treatment_label)\n", + "\n", + "# Plot params\n", + "plt.xlim([datetime(2022, 1, 1), x.max()])\n", + "plt.ylim([min(synth_delta) - 1, max(synth_delta) + 1])\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Delta from Pandemic\")\n", + "plt.title(\"Synthetic Control Deviation from Pandemic Baseline\")\n", + "plt.legend()\n", + "plt.ylim([-3, 2])\n", + "plt.savefig(FIG_OUT / \"MA-Pandemic-Delta.png\", dpi=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dashboard Figures\n", + "\n", + "## Dynamic Factor Model Runner\n", + "\n", + "![](imgs/1.png)\n", + "\n", + "## Factor Analysis\n", + "\n", + "![](imgs/2.png)\n", + "\n", + "## Comparative Run Analysis\n", + "\n", + "![](imgs/3.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py3.10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/reports/figures/imgs/1.png b/reports/figures/imgs/1.png new file mode 100644 index 0000000..1d1a8ed Binary files /dev/null and b/reports/figures/imgs/1.png differ diff --git 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+8/1/2016,0.8500476352643443,1.2665678805353493,252332.4136,16173.21683,32132.07632,115220.4253,48305.29314,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +9/1/2016,0.850047635264355,1.4450268294459392,252013.8683,16162.28301,32104.77739,115215.9868,48267.0604,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +10/1/2016,0.8500476352643471,1.8014041509324827,252191.8322,16143.68014,32062.26659,115156.589,48205.94672,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +11/1/2016,0.8500476352643525,0.4335747844288256,252828.8465,16154.54022,32078.28529,115307.1678,48232.82551,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +12/1/2016,0.8500476352643502,1.5131942576644963,253100.9265,16142.14841,32048.14459,115291.6195,48190.293,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +1/1/2017,0.8500476352643505,1.6197328582972768,252160.2725,16109.15838,32031.7955,115160.8354,48140.96179,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +2/1/2017,0.8500476352643493,-0.009763537606827466,251771.526,16111.28477,32085.01468,115280.5406,48196.31524,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +3/1/2017,0.8500476352643473,-1.5676771008962294,251908.1242,16147.00224,32205.08094,115640.4959,48352.10687,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +4/1/2017,0.8500476352643467,0.4967974638746142,253027.8471,16138.37822,32236.62808,115682.718,48375.03783,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +5/1/2017,0.8500476352643529,-1.357368576362492,254772.5309,16169.7678,32348.00972,116011.5658,48517.81694,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +6/1/2017,0.8500476352643473,-0.4230593283504791,256200.6332,16181.11799,32419.26948,116196.5695,48600.43472,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +7/1/2017,0.8500476352643511,-0.9423162167524419,256906.8045,16203.76757,32513.10916,116462.5889,48716.93184,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +8/1/2017,0.8500476352643501,0.6478252588305582,257068.7039,16192.14588,32538.05623,116482.0173,48730.26496,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +9/1/2017,0.8500476352643502,1.6846151302343408,256874.7594,16158.24342,32517.93579,116340.5358,48676.24964,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +10/1/2017,0.8500476352643536,-0.013156524110262247,257054.172,16161.03131,32571.40312,116462.6935,48732.51256,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +11/1/2017,0.8500476352643509,0.13087495522384496,257184.8974,16160.78477,32618.6052,116562.6648,48779.47578,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +12/1/2017,0.8500476352643535,-0.020141363016117353,257368.1062,16163.8624,32672.36867,116686.2965,48836.32455,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +1/1/2018,0.8500476352643482,1.9977289101628593,257562.7627,16160.87784,32600.88144,116691.5125,48761.84466,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +2/1/2018,0.8500476352643449,1.1626953506852762,258041.0376,16175.7801,32565.86914,116825.722,48741.72667,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +3/1/2018,0.8500476352643552,0.38950513939703324,258783.2283,16207.28449,32564.53139,117079.7341,48771.88547,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +4/1/2018,0.8500476352643529,0.8010047642987836,258184.7366,16229.82801,32545.40628,117268.9249,48775.29602,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +5/1/2018,0.8500476352643483,0.7596888887452765,257602.864,16253.18489,32528.15955,117463.8917,48781.39837,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +6/1/2018,0.8500476352643473,0.19962066199856565,257215.3956,16288.62505,32535.28994,117746.1063,48823.96115,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +7/1/2018,0.8500476352643496,0.035737021935908886,258404.0326,16327.59776,32549.60784,118053.8031,48877.24404,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +8/1/2018,0.8500476352643509,-0.06543934171476729,259626.3663,16368.76125,32568.4012,118377.2957,48937.19317,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +9/1/2018,0.8500476352643493,0.9791323275033477,260484.5592,16387.0726,32541.91267,118535.4476,48929.00826,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +10/1/2018,0.8500476352643459,0.6345993940779326,259468.5285,16412.83525,32530.46467,118747.3988,48943.31522,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +11/1/2018,0.8500476352643471,-0.33410346522077816,258790.3899,16459.78061,32561.13359,119112.5534,49020.92185,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +12/1/2018,0.850047635264346,-0.48932621776745194,258167.2799,16510.17056,32598.65493,119502.6195,49108.8255,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +1/1/2019,0.8500476352643507,0.38039210170315774,258350.9058,16518.75319,32621.08059,119704.846,49139.83379,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +2/1/2019,0.8500476352643491,1.6387639596052743,258101.1062,16499.62741,32588.78017,119706.0818,49088.40758,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +3/1/2019,0.8500476352643479,2.5144855638771104,257550.5057,16461.28578,32518.50278,119567.309,48979.78857,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +4/1/2019,0.8500476352643522,2.7821959459518375,257450.9625,16417.16767,32436.78266,119385.7715,48853.95032,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +5/1/2019,0.8500476352643557,0.9958514261946232,257963.4552,16412.21994,32432.43428,119488.5577,48844.65421,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +6/1/2019,0.8500476352643552,1.016118346686337,258468.1857,16406.83131,32427.20699,119587.9383,48834.0383,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +7/1/2019,0.8500476352643522,1.5609353087872895,258742.3474,16389.53228,32398.42771,119600.2053,48787.95999,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +8/1/2019,0.850047635264352,0.7361607694337968,259300.2942,16390.27107,32405.29546,119743.8528,48795.56653,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +9/1/2019,0.8500476352643515,0.9880404543814474,259770.6135,16385.50637,32401.27669,119847.1513,48786.78305,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +10/1/2019,0.8500476352643521,1.9458783889611002,260251.6384,16359.83144,32355.89506,119797.1437,48715.72649,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +11/1/2019,0.850047635264355,0.7901326394407362,261132.022,16359.39343,32360.41333,119931.6098,48719.80676,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +12/1/2019,0.8500476352643491,2.288084042508209,261488.7661,16326.30761,32300.33593,119826.3437,48626.64355,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +1/1/2020,0.8500399842943805,0.9299457669704125,258428.0959,16346.24225,32275.59249,119348.6504,48621.83474,0.0,0.0,0.0,0.0,0.0,0.0,34.0,34.0,30.0,30.0,0.0,0.0,CT +2/1/2020,0.8500427496703075,0.3764667589544253,255567.4199,16378.18493,32274.75166,118960.5054,48652.93659,0.0,0.0,0.0,0.0,0.0,0.0,74.0,54.0,34.0,30.0,0.0,0.0,CT +3/1/2020,0.17359601421215842,-2.134563315776668,253561.5029,16465.13842,32382.24572,118971.2517,48847.38414,3128.0,3128.0,69.0,69.0,3128.0,69.0,7618.0,7202.0,2990.0,2868.0,424.0,404.0,CT +4/1/2020,-20.56674556839444,-4.451081794130752,248152.2235,16603.68116,32590.7212,119350.0972,49194.40236,24572.0,23012.0,2188.0,1429.0,24572.0,2188.0,18643.0,16679.0,7834.0,7453.0,4869.0,3711.0,CT +5/1/2020,-15.663649017178983,0.32243888281874433,241139.0115,16637.04204,32592.46841,118970.244,49229.51045,14501.0,14049.0,1687.0,1389.0,14501.0,1687.0,7294.0,6469.0,2944.0,2844.0,1725.0,1333.0,CT +6/1/2020,-2.851084751290365,2.201862236824736,233554.8914,16628.6367,32512.68669,118294.5659,49141.32339,4313.0,4345.0,378.0,369.0,4313.0,378.0,59839.0,58597.0,811.0,791.0,92.0,64.0,CT +7/1/2020,-0.2268626535743497,1.7418030030354237,239221.1659,16630.31637,32453.03347,117694.6185,49083.34984,3296.0,3320.0,110.0,98.0,3296.0,110.0,8034.0,7783.0,315.0,301.0,0.0,0.0,CT +8/1/2020,0.5268895293501168,1.824934673298742,244825.7054,16630.04551,32389.94953,117084.5189,49019.99503,3069.0,2899.0,33.0,29.0,3069.0,33.0,7025.0,6635.0,391.0,379.0,0.0,0.0,CT +9/1/2020,0.42913693844649553,0.9003222172494983,250703.5919,16650.1886,32366.92858,116621.0925,49017.11718,4671.0,4376.0,43.0,30.0,4671.0,43.0,9173.0,8476.0,323.0,311.0,0.0,0.0,CT +10/1/2020,-0.2073137380163541,-0.2203338135634576,251793.0161,16695.19382,32392.37007,116333.0885,49087.56389,13657.0,12390.0,108.0,98.0,13657.0,108.0,32892.0,29315.0,629.0,501.0,0.0,0.0,CT +11/1/2020,-3.105214329907888,-0.3321645784446239,252920.2562,16742.71936,32422.73088,116062.9317,49165.45024,46088.0,41972.0,404.0,317.0,46088.0,404.0,82662.0,77338.0,3192.0,2889.0,264.0,178.0,CT +12/1/2020,-8.694791364648562,2.7921874374060014,252991.5292,16720.46366,32318.23451,115311.7254,49038.69817,68413.0,65188.0,975.0,829.0,68413.0,975.0,122539.0,118680.0,4044.0,3733.0,763.0,640.0,CT +1/1/2021,-9.438184323492306,-5.202438476040758,252226.92,16925.49793,32511.19174,115757.0933,49436.68227,64315.0,60135.0,1051.0,887.0,64315.0,1051.0,75508.0,73709.0,2660.0,2440.0,389.0,305.0,CT +2/1/2021,-4.78889061066077,-5.472342885949734,251590.608,17137.30107,32718.59314,116255.6251,49855.87946,29923.0,26238.0,576.0,513.0,29923.0,576.0,2310.0,2046.0,1569.0,1437.0,232.0,190.0,CT +3/1/2021,-1.7486633210712328,-3.4412281182414834,250311.444,17302.96043,32839.71505,116449.7897,50142.65348,30942.0,26183.0,264.0,223.0,30678.0,267.0,17839.0,15043.0,1186.0,1031.0,22.0,16.0,CT +4/1/2021,-1.2012165779167736,-3.7929530707873274,251760.4183,17476.61315,32978.16839,116708.0423,50454.75236,28345.0,24035.0,211.0,182.0,28609.0,208.0,802.0,650.0,32.0,18.0,122.0,90.0,CT +5/1/2021,-0.5303547268277669,-3.8785696847397615,253249.2299,17652.2759,33122.37086,116989.0364,50774.61047,8273.0,6920.0,141.0,118.0,8108.0,141.0,207251.0,176713.0,165.0,155.0,0.0,0.0,CT +6/1/2021,0.45849919778790127,-3.424536281837338,254595.0909,17817.21492,33248.41068,117208.2922,51065.58229,1846.0,1462.0,40.0,29.0,2011.0,40.0,11981.0,10375.0,17.0,13.0,0.0,0.0,CT +7/1/2021,0.7031405641166426,-4.07038213854374,254926.8302,17997.54568,33405.00147,117537.5117,51402.49685,5397.0,4200.0,15.0,15.0,4983.0,15.0,23255.0,19258.0,37.0,33.0,0.0,0.0,CT +8/1/2021,0.21367518132443186,-4.444269045573561,255409.0092,18187.16865,33580.34763,117934.6009,51767.45904,18171.0,15248.0,65.0,61.0,18737.0,65.0,33064.0,27220.0,74.0,66.0,0.0,0.0,CT +9/1/2021,-1.8021154112204893,-4.968762285844072,256093.3326,18390.09314,33781.48329,118423.8023,52171.51223,17273.0,14399.0,271.0,170.0,17273.0,271.0,39510.0,33358.0,55.0,51.0,0.0,0.0,CT +10/1/2021,-0.4717251622601855,-2.925788999700517,256608.4435,18543.17225,33892.56803,118599.2248,52435.66935,12238.0,11435.0,135.0,130.0,12238.0,135.0,258.0,236.0,22.0,16.0,53.0,48.0,CT +11/1/2021,-0.5301488844380238,-2.546770257964052,257008.9397,18686.60462,33988.00761,118722.4126,52674.53469,19109.0,17953.0,141.0,114.0,19109.0,141.0,19826.0,18428.0,15.0,13.0,0.0,0.0,CT +12/1/2021,-1.64631154799111,-2.9948573296465186,257577.8561,18840.97239,34105.23304,118924.1087,52946.12129,88496.0,74788.0,255.0,200.0,88496.0,255.0,314.0,282.0,11.0,11.0,162.0,159.0,CT +1/1/2022,-7.4705062583101105,0.6223335730290174,258018.1749,18817.00832,34145.74771,119037.5537,52962.6793,189212.0,169848.0,850.0,659.0,189212.0,850.0,1537.0,1402.0,103.0,97.0,784.0,734.0,CT +2/1/2022,-3.652900005022513,1.130011273405239,258279.6907,18780.62818,34162.79741,119069.5052,52943.35624,23989.0,20412.0,460.0,417.0,23989.0,460.0,55808.0,48167.0,32.0,32.0,240.0,224.0,CT +3/1/2022,-2.159500253315799,3.813552375000249,257613.2739,18677.6197,34057.37671,118675.0233,52734.9346,10203.0,8854.0,306.0,257.0,12644.0,309.0,67950.0,59635.0,15.0,15.0,34.0,32.0,CT +4/1/2022,0.1396797572794748,-1.7676193094662391,257771.3065,18714.21951,34205.58339,119164.6307,52919.74806,21506.0,19029.0,74.0,53.0,22248.0,71.0,122.0,104.0,2.0,2.0,86.0,76.0,CT +5/1/2022,-0.04113818563306165,1.2927559791750607,256884.7642,18674.78196,34214.15917,119168.009,52888.89343,42291.0,37916.0,91.0,72.0,50913.0,91.0,300578.0,268132.0,109.0,109.0,104.0,96.0,CT +6/1/2022,-0.167871980374231,4.17658286254289,255024.2891,18564.14761,34091.0195,118713.04,52655.12663,19774.0,16496.0,104.0,82.0,19774.0,104.0,10303.0,8936.0,14.0,12.0,44.0,42.0,CT +7/1/2022,0.29202413165363317,-3.8943533608988457,256853.8133,18654.13395,34335.58622,119538.7449,52989.68641,22925.0,19927.0,57.0,40.0,25614.0,57.0,183.0,154.0,38.0,34.0,48.0,48.0,CT +8/1/2022,-0.7848331350226601,-1.165379326843623,257754.2674,18676.72098,34455.95715,119932.1091,53132.65119,20494.0,18739.0,167.0,143.0,20494.0,167.0,111216.0,98765.0,606.0,602.0,0.0,0.0,CT +9/1/2022,-0.08952566221780256,-0.5753084195748432,258452.0032,18684.87926,34549.22918,120231.3055,53234.08831,21817.0,16021.0,96.0,47.0,21817.0,96.0,12262.0,12238.0,369.0,368.0,0.0,0.0,CT +10/1/2022,0.7485676036870412,-0.30419714703461675,259645.1966,18686.53191,34629.91048,120486.8684,53316.42902,4287.0,3508.0,20.0,11.0,0.0,0.0,6691.0,6677.0,155.0,155.0,0.0,0.0,CT +11/1/2022,0.849693876584184,-2.382649411602179,261557.0822,18740.30682,34806.8177,121077.3464,53547.11783,0.0,0.0,0.0,0.0,0.0,,328.0,322.0,11.0,11.0,0.0,0.0,CT +12/1/2022,0.8500211038515721,-2.698768168265974,263583.4756,18802.41008,34999.07859,121721.2701,53801.48867,0.0,0.0,0.0,0.0,0.0,,114.0,104.0,44.0,44.0,59.0,55.0,CT diff --git a/reports/figures/results/CT/model.csv b/reports/figures/results/CT/model.csv new file mode 100644 index 0000000..e36e891 --- /dev/null +++ b/reports/figures/results/CT/model.csv @@ -0,0 +1,29 @@ + Model Specification: Dynamic Factor Model +Model: ,Dynamic Factor Model , # of observed variables: ,17 + ,+ 2 factors in 2 blocks, # of factor blocks: ,2 + ,+ AR(1) idiosyncratic , Idiosyncratic disturbances:,AR(1) +Sample:,2012-02-01 00:00:00 , Standardize variables: ,True + ,- 2022-12-01 00:00:00 , , +Observed variables / factor loadings +Dep. variable,Pandemic,Consumption +GDP , ,X +Cons3 , ,X +Cons4 , ,X +Cons5 , ,X +Cons2 , ,X +Cases5 ,X , +Cases2 ,X , +Deaths5 ,X , +Deaths2 ,X , +Cases3 ,X , +Deaths3 ,X , +Cases4 ,X , +Cases1 ,X , +Hosp2 ,X , +Hosp1 ,X , +Deaths4 ,X , +Deaths1 ,X , + Factor blocks: + block ,order +Pandemic ,1 +Consumption,1 diff --git a/reports/figures/results/CT/raw.csv b/reports/figures/results/CT/raw.csv new file mode 100644 index 0000000..1b7eb2a --- /dev/null +++ b/reports/figures/results/CT/raw.csv @@ -0,0 +1,132 @@ 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Variable: ,"GDP", and 16 more , No. Observations: ,131 +Model: ,Dynamic Factor Model , Log Likelihood ,-1003.279 + ,+ 2 factors in 2 blocks, AIC ,2116.558 + ,+ AR(1) idiosyncratic , BIC ,2274.694 +Date: ,Sat, 26 Oct 2024 , HQIC ,2180.816 +Time: ,18:48:33 , EM Iterations ,261 +Sample: ,02-01-2012 , , + ,- 12-01-2022 , , +Covariance Type:,Not computed , , + Observation equation: +Factor loadings:,Pandemic,Consumption, idiosyncratic: AR(1),var. +GDP ,. ,-0.10 ,0.58 ,0.66 +Cons3 ,. ,-0.48 ,0.87 ,0.04 +Cons4 ,. ,-0.52 ,0.87 ,0.02 +Cons5 ,. ,-0.44 ,0.90 ,0.10 +Cons2 ,. ,-0.53 ,0.91 ,0.00 +Cases5 ,-0.24 ,. ,0.50 ,0.56 +Cases2 ,-0.24 ,. ,0.48 ,0.57 +Deaths5 ,-0.35 ,. ,-0.05 ,0.00 +Deaths2 ,-0.35 ,. ,0.04 ,0.02 +Cases3 ,-0.24 ,. ,0.50 ,0.57 +Deaths3 ,-0.35 ,. ,-0.05 ,0.00 +Cases4 ,-0.13 ,. ,0.05 ,0.95 +Cases1 ,-0.14 ,. ,0.06 ,0.94 +Hosp2 ,-0.32 ,. ,0.38 ,0.23 +Hosp1 ,-0.32 ,. ,0.38 ,0.23 +Deaths4 ,-0.31 ,. ,0.31 ,0.22 +Deaths1 ,-0.32 ,. ,0.33 ,0.21 + Transition: Factor block 0 + ,L1.Pandemic, error variance +Pandemic,0.63 ,4.80 + Transition: Factor block 1 + ,L1.Consumption, error variance +Consumption,0.56 ,2.41 + +Warnings: +[1] Covariance matrix not calculated. diff --git a/reports/figures/results/CT/run-info.yaml b/reports/figures/results/CT/run-info.yaml new file mode 100644 index 0000000..7a81bae --- /dev/null +++ b/reports/figures/results/CT/run-info.yaml @@ -0,0 +1,45 @@ +diff_cols: [] +factor_map: + Cases1: !!python/tuple + - Pandemic + Cases2: !!python/tuple + - Pandemic + Cases3: !!python/tuple + - Pandemic + Cases4: !!python/tuple + - Pandemic + Cases5: !!python/tuple + - Pandemic + Cons2: !!python/tuple + - Consumption + Cons3: !!python/tuple + - Consumption + Cons4: !!python/tuple + - Consumption + Cons5: !!python/tuple + - Consumption + Deaths1: !!python/tuple + - Pandemic + Deaths2: !!python/tuple + - Pandemic + Deaths3: !!python/tuple + - Pandemic + Deaths4: !!python/tuple + - Pandemic + Deaths5: !!python/tuple + - Pandemic + GDP: !!python/tuple + - Consumption + Hosp1: !!python/tuple + - Pandemic + Hosp2: !!python/tuple + - Pandemic +global_multiplier: 0 +logdiff_cols: +- GDP +- Cons3 +- Cons4 +- Cons5 +- Cons2 +maxiter: 10000 +non_stationary_cols: null diff --git a/reports/figures/results/IL/df.csv b/reports/figures/results/IL/df.csv new file mode 100644 index 0000000..7fa0e57 --- /dev/null +++ b/reports/figures/results/IL/df.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,0.6925804659570864,0.25045372985259,0.19474955490044024,0.48893226629655495,0.22091550523534448,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +3/1/2012,0.6974646085891172,0.26638171365966623,0.22136992367389405,0.5073703001100136,0.2424843322376093,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +4/1/2012,0.6304377837477828,0.295627340732172,0.27008473635114505,0.5411958882116524,0.2819925619212286,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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b/reports/figures/results/IL/model.csv new file mode 100644 index 0000000..e36e891 --- /dev/null +++ b/reports/figures/results/IL/model.csv @@ -0,0 +1,29 @@ + Model Specification: Dynamic Factor Model +Model: ,Dynamic Factor Model , # of observed variables: ,17 + ,+ 2 factors in 2 blocks, # of factor blocks: ,2 + ,+ AR(1) idiosyncratic , Idiosyncratic disturbances:,AR(1) +Sample:,2012-02-01 00:00:00 , Standardize variables: ,True + ,- 2022-12-01 00:00:00 , , +Observed variables / factor loadings +Dep. variable,Pandemic,Consumption +GDP , ,X +Cons3 , ,X +Cons4 , ,X +Cons5 , ,X +Cons2 , ,X +Cases5 ,X , +Cases2 ,X , +Deaths5 ,X , +Deaths2 ,X , +Cases3 ,X , +Deaths3 ,X , +Cases4 ,X , +Cases1 ,X , +Hosp2 ,X , +Hosp1 ,X , +Deaths4 ,X , +Deaths1 ,X , + Factor blocks: + block ,order +Pandemic ,1 +Consumption,1 diff --git a/reports/figures/results/IL/raw.csv b/reports/figures/results/IL/raw.csv new file mode 100644 index 0000000..af4f0f6 --- /dev/null +++ b/reports/figures/results/IL/raw.csv @@ 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Variable: ,"GDP", and 16 more , No. Observations: ,131 +Model: ,Dynamic Factor Model , Log Likelihood ,-1200.816 + ,+ 2 factors in 2 blocks, AIC ,2511.631 + ,+ AR(1) idiosyncratic , BIC ,2669.767 +Date: ,Sat, 26 Oct 2024 , HQIC ,2575.889 +Time: ,18:48:34 , EM Iterations ,241 +Sample: ,02-01-2012 , , + ,- 12-01-2022 , , +Covariance Type:,Not computed , , + Observation equation: +Factor loadings:,Pandemic,Consumption, idiosyncratic: AR(1),var. +GDP ,. ,-0.12 ,0.57 ,0.67 +Cons3 ,. ,-0.52 ,0.91 ,0.02 +Cons4 ,. ,-0.56 ,0.91 ,0.01 +Cons5 ,. ,-0.29 ,0.87 ,0.22 +Cons2 ,. ,-0.56 ,0.94 ,0.00 +Cases5 ,-0.29 ,. ,-0.25 ,0.15 +Cases2 ,-0.29 ,. ,-0.26 ,0.15 +Deaths5 ,-0.28 ,. ,0.50 ,0.36 +Deaths2 ,-0.28 ,. ,0.53 ,0.36 +Cases3 ,-0.29 ,. ,-0.29 ,0.17 +Deaths3 ,-0.28 ,. ,0.50 ,0.36 +Cases4 ,-0.29 ,. ,0.15 ,0.05 +Cases1 ,-0.29 ,. ,0.17 ,0.05 +Hosp2 ,-0.30 ,. ,0.75 ,0.07 +Hosp1 ,-0.30 ,. ,0.75 ,0.07 +Deaths4 ,-0.28 ,. ,0.72 ,0.20 +Deaths1 ,-0.27 ,. ,0.71 ,0.22 + Transition: Factor block 0 + ,L1.Pandemic, error variance +Pandemic,0.76 ,4.19 + Transition: Factor block 1 + ,L1.Consumption, error variance +Consumption,0.64 ,1.81 + +Warnings: +[1] Covariance matrix not calculated. diff --git a/reports/figures/results/IL/run-info.yaml b/reports/figures/results/IL/run-info.yaml new file mode 100644 index 0000000..7a81bae --- /dev/null +++ b/reports/figures/results/IL/run-info.yaml @@ -0,0 +1,45 @@ +diff_cols: [] +factor_map: + Cases1: !!python/tuple + - Pandemic + Cases2: !!python/tuple + - Pandemic + Cases3: !!python/tuple + - Pandemic + Cases4: !!python/tuple + - Pandemic + Cases5: !!python/tuple + - Pandemic + Cons2: !!python/tuple + - Consumption + Cons3: !!python/tuple + - Consumption + Cons4: !!python/tuple + - Consumption + Cons5: !!python/tuple + - Consumption + Deaths1: !!python/tuple + - Pandemic + Deaths2: !!python/tuple + - Pandemic + Deaths3: !!python/tuple + - Pandemic + Deaths4: !!python/tuple + - Pandemic + Deaths5: !!python/tuple + - Pandemic + GDP: !!python/tuple + - Consumption + Hosp1: !!python/tuple + - Pandemic + Hosp2: !!python/tuple + - Pandemic +global_multiplier: 0 +logdiff_cols: +- GDP +- Cons3 +- Cons4 +- Cons5 +- Cons2 +maxiter: 10000 +non_stationary_cols: null diff --git a/reports/figures/results/MA/df.csv b/reports/figures/results/MA/df.csv new file mode 100644 index 0000000..faa5237 --- /dev/null +++ b/reports/figures/results/MA/df.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,0.6112605074147085,0.3053078613252142,0.27859679171555124,0.5319085813175483,0.28376912360669315,0.0,0.0,0.0,0.0,0.0,0.07361963190184048,0.0,0.0,0.0,0.0,0.0,0.0 +3/1/2012,0.6169860341346105,0.3225025866798784,0.30757083056955287,0.5501492324500485,0.30835391943018364,0.0,0.0,0.0,0.0,0.0,0.07361963190184048,0.0,0.0,0.0,0.0,0.0,0.0 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+10/1/2019,0.6940804898775274,1.007114628374088,539572.2811,31595.54622,64430.88547,247888.6796,96026.44678,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +11/1/2019,0.6939043238599645,-0.1546355297400197,541085.1913,31633.60309,64573.5078,248472.8823,96207.11844,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +12/1/2019,0.6937371816075162,1.3602273814443022,541513.9611,31608.42003,64586.85522,248559.547,96195.27525,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +1/1/2020,0.676874938934808,1.5198750408753483,541104.9118,31657.07474,64506.59792,246962.1085,96163.67266,0.0,0.0,0.0,0.0,1.0,0.0,174.0,64.0,10.0,6.0,0.0,0.0,MA +2/1/2020,0.574921790307672,0.9609460933946716,541096.5757,31728.95471,64474.20546,245551.7992,96203.16017,1.0,1.0,0.0,0.0,1.0,0.0,526.0,200.0,64.0,56.0,0.0,0.0,MA +3/1/2020,-9.874415625755297,-1.572351231722454,542904.5153,31907.41626,64658.19678,244964.2,96565.61304,6619.0,6619.0,155.0,153.0,9922.0,89.0,28949.0,26475.0,5304.0,5246.0,1118.0,1108.0,MA +4/1/2020,-26.943943358133147,-3.9089243098137914,530405.9436,32185.92669,65043.58791,245129.9171,97229.51459,55585.0,55585.0,3970.0,3858.0,56943.0,3473.0,112398.0,110680.0,13292.0,13162.0,10450.0,10074.0,MA +5/1/2020,-8.324349056755679,0.9056025948729527,514475.0936,32260.58648,65016.26598,243734.7497,97276.85246,34760.0,34760.0,3114.0,3032.0,30393.0,3284.0,62015.0,57479.0,4363.0,4235.0,2882.0,2670.0,MA +6/1/2020,-1.1169285980985357,2.8007694798549903,497330.6485,32254.21234,64826.44971,241736.3607,97080.66206,6736.0,6736.0,887.0,883.0,7180.0,1208.0,16197.0,13536.0,950.0,908.0,220.0,194.0,MA +7/1/2020,-0.3699493765061739,2.3358564275149005,508364.1542,32267.3356,64676.96629,239896.7261,96944.30189,6086.0,6086.0,500.0,495.0,7396.0,555.0,18364.0,13822.0,616.0,584.0,60.0,48.0,MA +8/1/2020,-0.29811550488190797,2.4190040664553774,519272.4836,32276.61541,64520.82828,238040.0798,96797.4437,8997.0,8997.0,442.0,441.0,9182.0,-276.0,20830.0,17666.0,614.0,590.0,0.0,0.0,MA +9/1/2020,-0.6957860096765582,1.4857245999037385,530765.323,32325.46836,64444.64355,236484.6165,96770.1119,11627.0,10969.0,418.0,410.0,11098.0,115.0,26242.0,24900.0,840.0,820.0,0.0,0.0,MA +10/1/2020,-1.725257229836906,0.354742800804547,536237.0426,32422.56915,64465.01505,235286.1822,96887.58419,26460.0,24768.0,542.0,531.0,26460.0,223.0,56871.0,53687.0,1384.0,1332.0,290.0,262.0,MA +11/1/2020,-5.199616217297425,0.24139981435147595,541787.8017,32524.55985,64495.19102,234124.1366,97019.75086,67556.0,63808.0,820.0,810.0,67556.0,479.0,156716.0,148893.0,3100.0,3010.0,1316.0,1244.0,MA +12/1/2020,-8.85352044831534,3.3921401292386584,545062.5056,32490.94962,64257.24476,231994.4717,96748.19438,149046.0,141116.0,1765.0,1733.0,149046.0,1372.0,302108.0,286018.0,4786.0,4704.0,3168.0,3032.0,MA +1/1/2021,-6.816465933393343,-5.318128670820618,545637.5889,32913.62077,64681.37581,233256.5826,97594.99658,148847.0,138700.0,2219.0,2189.0,148847.0,1756.0,275294.0,256052.0,3783.0,3659.0,2348.0,2206.0,MA +2/1/2021,-2.0200344176377083,-5.578364085438159,546474.2109,33349.28279,65133.95479,234623.2033,98483.23758,57123.0,52157.0,1387.0,1359.0,57123.0,1074.0,108287.0,98711.0,1516.0,1434.0,452.0,400.0,MA +3/1/2021,-1.2605600065994516,-3.5181552861136067,545894.0263,33694.93189,65414.40791,235372.2323,99109.3398,54432.0,47875.0,1024.0,1006.0,54432.0,461.0,116134.0,103769.0,1180.0,1132.0,76.0,72.0,MA +4/1/2021,-0.8009900380875212,-3.8618050093575533,548143.2331,34055.88917,65728.946,236246.8122,99784.83517,53393.0,47690.0,367.0,359.0,53393.0,339.0,99269.0,88612.0,1010.0,966.0,24.0,24.0,MA +5/1/2021,0.2684652130944518,-3.937454231404132,550488.537,34420.52999,66054.54382,237163.7897,100475.0738,18064.0,15337.0,262.0,249.0,17972.0,245.0,29730.0,26078.0,512.0,500.0,0.0,0.0,MA +6/1/2021,0.9023174172867798,-3.469166952472686,552532.5226,34764.02675,66343.51886,237951.9392,101107.5456,2929.0,2618.0,122.0,122.0,3021.0,108.0,6280.0,5425.0,211.0,204.0,0.0,0.0,MA +7/1/2021,0.19077036498357103,-4.110681205605424,554838.6587,35137.33698,66693.0779,238959.8817,101830.4149,10515.0,9350.0,88.0,84.0,9814.0,105.0,26148.0,23494.0,571.0,556.0,0.0,0.0,MA +8/1/2021,-1.550378110474643,-4.4781440727018476,557458.8071,35528.60889,67079.76939,240102.9393,102608.3783,39388.0,36427.0,164.0,160.0,40089.0,169.0,84748.0,78247.0,1462.0,1424.0,168.0,158.0,MA +9/1/2021,-1.7993031312844199,-4.997807249355386,560509.1261,35945.71862,67517.72139,241431.1734,103463.44,52082.0,48250.0,381.0,366.0,52082.0,362.0,102063.0,95056.0,1638.0,1587.0,244.0,234.0,MA +10/1/2021,-1.3329492310516278,-2.92809944539125,561292.4579,36265.21903,67775.37546,242116.7897,104040.5945,39971.0,36533.0,369.0,361.0,39971.0,355.0,81837.0,75349.0,1457.0,1409.0,314.0,300.0,MA +11/1/2021,-3.052278922990441,-2.5371547555792975,561829.1314,36565.61619,68001.32544,242691.9134,104566.9416,67175.0,61060.0,408.0,390.0,67175.0,385.0,154677.0,141594.0,2346.0,2270.0,606.0,578.0,MA +12/1/2021,-6.632203327825545,-2.9807527762932855,562737.8598,36887.1854,68270.46221,243423.8184,105157.6476,221517.0,204521.0,869.0,830.0,221517.0,825.0,501255.0,464535.0,4115.0,3976.0,1488.0,1394.0,MA +1/1/2022,-9.31890156674927,0.7287744038192088,561619.388,36859.42239,68374.18075,243892.6695,105233.6101,470980.0,425660.0,1761.0,1598.0,470980.0,1690.0,883420.0,795155.0,5337.0,5139.0,3142.0,2810.0,MA +2/1/2022,-0.67377126241532,1.244359739446948,560134.8278,36807.12499,68430.66119,244191.9209,105237.8,59698.0,53903.0,1390.0,1267.0,59698.0,1248.0,100307.0,90085.0,1227.0,1167.0,237.0,186.0,MA +3/1/2022,0.6658914094304837,3.9544492825606614,556669.1669,36623.95628,68241.48334,243613.0596,104865.4602,27469.0,24868.0,460.0,89.0,27469.0,461.0,56987.0,51405.0,594.0,583.0,22.0,16.0,MA +4/1/2022,-0.701723021917493,-1.6717437362610736,558691.5152,36714.32371,68560.25271,244846.4034,105274.6038,58342.0,53612.0,158.0,140.0,58342.0,158.0,130776.0,119067.0,1291.0,1257.0,103.0,84.0,MA +5/1/2022,-2.528416080316953,1.418456191367226,558436.4132,36655.36931,68598.97792,245078.8771,105254.3813,113733.0,99964.0,331.0,274.0,113733.0,331.0,224153.0,195106.0,2224.0,2168.0,442.0,359.0,MA +6/1/2022,-0.4279017032717558,4.330315636433059,556037.7129,36456.37671,68373.27547,244365.1629,104829.6927,53866.0,47689.0,350.0,299.0,53866.0,350.0,102941.0,90738.0,1246.0,1226.0,224.0,164.0,MA +7/1/2022,-0.9594790331535092,-3.807109532028808,557693.7851,36651.20168,68884.85944,246285.6742,105536.1084,42217.0,37732.0,232.0,189.0,42217.0,232.0,95546.0,84753.0,1555.0,1516.0,219.0,158.0,MA +8/1/2022,-0.19105319145043964,-1.0511498885872634,557336.6578,36713.57063,69147.24234,247315.0513,105860.8668,38331.0,34772.0,214.0,165.0,38331.0,214.0,71641.0,63653.0,1219.0,1177.0,182.0,143.0,MA +9/1/2022,0.5980900203642696,-0.452772340028618,556555.2459,36747.46686,69355.11542,248148.9118,106102.6427,45692.0,40487.0,316.0,229.0,45692.0,316.0,40967.0,35884.0,828.0,811.0,187.0,143.0,MA +10/1/2022,0.6338559606410343,-0.17633418715894877,560275.9523,36768.44028,69537.5653,248891.1624,106306.0724,9942.0,8480.0,65.0,49.0,0.0,0.0,34834.0,29760.0,815.0,792.0,323.0,245.0,MA +11/1/2022,0.9085575687198677,-2.2696963612496206,565542.0702,36891.88792,69913.14212,250324.2374,106805.1035,0.0,0.0,0.0,0.0,0.0,,26856.0,22463.0,679.0,657.0,215.0,155.0,MA +12/1/2022,0.09828410023313294,-2.5854482579834213,571054.0907,37031.70444,70319.52824,251867.4935,107351.3128,0.0,0.0,0.0,0.0,0.0,,46798.0,39197.0,1074.0,1043.0,388.0,293.0,MA diff --git a/reports/figures/results/MA/model.csv b/reports/figures/results/MA/model.csv new file mode 100644 index 0000000..e36e891 --- /dev/null +++ b/reports/figures/results/MA/model.csv @@ -0,0 +1,29 @@ + Model Specification: Dynamic Factor Model +Model: ,Dynamic Factor Model , # of observed variables: ,17 + ,+ 2 factors in 2 blocks, # of factor blocks: ,2 + ,+ AR(1) idiosyncratic , Idiosyncratic disturbances:,AR(1) +Sample:,2012-02-01 00:00:00 , Standardize variables: ,True + ,- 2022-12-01 00:00:00 , , +Observed variables / factor loadings +Dep. variable,Pandemic,Consumption +GDP , ,X +Cons3 , ,X +Cons4 , ,X +Cons5 , ,X +Cons2 , ,X +Cases5 ,X , +Cases2 ,X , +Deaths5 ,X , +Deaths2 ,X , +Cases3 ,X , +Deaths3 ,X , +Cases4 ,X , +Cases1 ,X , +Hosp2 ,X , +Hosp1 ,X , +Deaths4 ,X , +Deaths1 ,X , + Factor blocks: + block ,order +Pandemic ,1 +Consumption,1 diff --git a/reports/figures/results/MA/raw.csv b/reports/figures/results/MA/raw.csv new file mode 100644 index 0000000..3e9d1e8 --- /dev/null +++ b/reports/figures/results/MA/raw.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,437236.0574,27727.81154,58369.04583,203748.874,86096.85737,0.0,0.0,0.0,0.0,0.0,,,,,,, +3/1/2012,437479.8215,27743.14433,58353.48674,203852.9854,86096.63107,0.0,0.0,0.0,0.0,0.0,,,,,,, +4/1/2012,438989.9799,27774.81826,58372.45242,204077.1968,86147.27068,0.0,0.0,0.0,0.0,0.0,,,,,,, +5/1/2012,441569.227,27874.1454,58533.60935,204798.496,86407.75474,0.0,0.0,0.0,0.0,0.0,,,,,,, +6/1/2012,443878.0791,27956.30065,58658.62954,205393.6151,86614.93019,0.0,0.0,0.0,0.0,0.0,,,,,,, +7/1/2012,444952.2759,28014.36234,58733.08937,205811.7188,86747.45171,0.0,0.0,0.0,0.0,0.0,,,,,,, +8/1/2012,444693.0802,27988.50862,58631.79282,205613.3566,86620.30144,0.0,0.0,0.0,0.0,0.0,,,,,,, +9/1/2012,444572.8688,27971.46315,58549.24835,205479.7567,86520.7115,0.0,0.0,0.0,0.0,0.0,,,,,,, +10/1/2012,443345.4844,27956.18662,58470.68678,205359.2015,86426.8734,0.0,0.0,0.0,0.0,0.0,,,,,,, +11/1/2012,443733.7283,28042.64529,58605.01214,205985.987,86647.65743,0.0,0.0,0.0,0.0,0.0,,,,,,, +12/1/2012,443892.915,28114.67995,58709.15471,206506.8146,86823.83466,0.0,0.0,0.0,0.0,0.0,,,,,,, +1/1/2013,443749.723,28128.98324,58693.24251,206657.6047,86822.22575,0.0,0.0,0.0,0.0,0.0,,,,,,, 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+11/1/2022,565542.0702,36891.88792,69913.14212,250324.2374,106805.1035,0.0,0.0,0.0,0.0,0.0,,26856.0,22463.0,679.0,657.0,215.0,155.0 +12/1/2022,571054.0907,37031.70444,70319.52824,251867.4935,107351.3128,0.0,0.0,0.0,0.0,0.0,,46798.0,39197.0,1074.0,1043.0,388.0,293.0 diff --git a/reports/figures/results/MA/results.csv b/reports/figures/results/MA/results.csv new file mode 100644 index 0000000..12d98c7 --- /dev/null +++ b/reports/figures/results/MA/results.csv @@ -0,0 +1,38 @@ + Dynamic Factor Results +Dep. Variable: ,"GDP", and 16 more , No. Observations: ,131 +Model: ,Dynamic Factor Model , Log Likelihood ,-1107.173 + ,+ 2 factors in 2 blocks, AIC ,2324.346 + ,+ AR(1) idiosyncratic , BIC ,2482.482 +Date: ,Sat, 26 Oct 2024 , HQIC ,2388.604 +Time: ,18:48:46 , EM Iterations ,251 +Sample: ,02-01-2012 , , + ,- 12-01-2022 , , +Covariance Type:,Not computed , , + Observation equation: +Factor loadings:,Pandemic,Consumption, idiosyncratic: AR(1),var. +GDP ,. ,-0.10 ,0.56 ,0.68 +Cons3 ,. ,-0.48 ,0.89 ,0.03 +Cons4 ,. ,-0.53 ,0.89 ,0.02 +Cons5 ,. ,-0.42 ,0.94 ,0.06 +Cons2 ,. ,-0.54 ,0.94 ,0.00 +Cases5 ,-0.28 ,. ,0.51 ,0.60 +Cases2 ,-0.28 ,. ,0.51 ,0.59 +Deaths5 ,-0.30 ,. ,0.35 ,0.24 +Deaths2 ,-0.30 ,. ,0.34 ,0.24 +Cases3 ,-0.28 ,. ,0.51 ,0.60 +Deaths3 ,-0.29 ,. ,0.29 ,0.29 +Cases4 ,-0.28 ,. ,0.54 ,0.56 +Cases1 ,-0.28 ,. ,0.55 ,0.54 +Hosp2 ,-0.30 ,. ,1.03 ,0.00 +Hosp1 ,-0.30 ,. ,1.03 ,0.00 +Deaths4 ,-0.28 ,. ,-0.14 ,0.09 +Deaths1 ,-0.28 ,. ,-0.14 ,0.09 + Transition: Factor block 0 + ,L1.Pandemic, error variance +Pandemic,0.61 ,6.16 + Transition: Factor block 1 + ,L1.Consumption, error variance +Consumption,0.52 ,2.47 + +Warnings: +[1] Covariance matrix not calculated. diff --git a/reports/figures/results/MA/run-info.yaml b/reports/figures/results/MA/run-info.yaml new file mode 100644 index 0000000..7a81bae --- /dev/null +++ b/reports/figures/results/MA/run-info.yaml @@ -0,0 +1,45 @@ +diff_cols: [] +factor_map: + Cases1: !!python/tuple + - Pandemic + Cases2: !!python/tuple + - Pandemic + Cases3: !!python/tuple + - Pandemic + Cases4: !!python/tuple + - Pandemic + Cases5: !!python/tuple + - Pandemic + Cons2: !!python/tuple + - Consumption + Cons3: !!python/tuple + - Consumption + Cons4: !!python/tuple + - Consumption + Cons5: !!python/tuple + - Consumption + Deaths1: !!python/tuple + - Pandemic + Deaths2: !!python/tuple + - Pandemic + Deaths3: !!python/tuple + - Pandemic + Deaths4: !!python/tuple + - Pandemic + Deaths5: !!python/tuple + - Pandemic + GDP: !!python/tuple + - Consumption + Hosp1: !!python/tuple + - Pandemic + Hosp2: !!python/tuple + - Pandemic +global_multiplier: 0 +logdiff_cols: +- GDP +- Cons3 +- Cons4 +- Cons5 +- Cons2 +maxiter: 10000 +non_stationary_cols: null diff --git a/reports/figures/results/ME/df.csv b/reports/figures/results/ME/df.csv new file mode 100644 index 0000000..9c0344f --- /dev/null +++ b/reports/figures/results/ME/df.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,0.4449949496516967,0.22495611384918449,0.16932591162376354,0.2891489602784704,0.1068005974119196,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +3/1/2012,0.45143120047180285,0.24142328711326183,0.1943829695322077,0.31154895109684555,0.13201563086983262,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +4/1/2012,0.5369043465004599,0.27146883086221135,0.24009806065521996,0.35241694785297506,0.1780198901940181,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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+11/1/2022,-0.24796008911659595,-2.258855359435397,70230.95299,6784.267513,14704.34784,40064.15138,21488.61535,0.0,0.0,0.0,0.0,0.0,,3840.0,2518.0,154.0,151.0,0.0,0.0,ME +12/1/2022,0.05235368945808583,-2.529628286548578,70966.0361,6804.717609,14810.06979,40309.35269,21614.7874,0.0,0.0,0.0,0.0,0.0,,5001.0,3376.0,211.0,207.0,0.0,0.0,ME diff --git a/reports/figures/results/ME/model.csv b/reports/figures/results/ME/model.csv new file mode 100644 index 0000000..e36e891 --- /dev/null +++ b/reports/figures/results/ME/model.csv @@ -0,0 +1,29 @@ + Model Specification: Dynamic Factor Model +Model: ,Dynamic Factor Model , # of observed variables: ,17 + ,+ 2 factors in 2 blocks, # of factor blocks: ,2 + ,+ AR(1) idiosyncratic , Idiosyncratic disturbances:,AR(1) +Sample:,2012-02-01 00:00:00 , Standardize variables: ,True + ,- 2022-12-01 00:00:00 , , +Observed variables / factor loadings +Dep. variable,Pandemic,Consumption +GDP , ,X +Cons3 , ,X +Cons4 , ,X +Cons5 , ,X +Cons2 , ,X +Cases5 ,X , +Cases2 ,X , +Deaths5 ,X , +Deaths2 ,X , +Cases3 ,X , +Deaths3 ,X , +Cases4 ,X , +Cases1 ,X , +Hosp2 ,X , +Hosp1 ,X , +Deaths4 ,X , +Deaths1 ,X , + Factor blocks: + block ,order +Pandemic ,1 +Consumption,1 diff --git a/reports/figures/results/ME/raw.csv b/reports/figures/results/ME/raw.csv new file mode 100644 index 0000000..6a2c613 --- /dev/null +++ b/reports/figures/results/ME/raw.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,53664.70775,4697.85859,11807.20612,32818.87589,16505.06471,0.0,0.0,0.0,0.0,0.0,,,,,,, +3/1/2012,53595.52815,4690.905901,11788.96576,32768.46142,16479.87166,0.0,0.0,0.0,0.0,0.0,,,,,,, +4/1/2012,53758.23604,4686.746908,11777.74885,32737.56834,16464.49576,0.0,0.0,0.0,0.0,0.0,,,,,,, +5/1/2012,54051.86759,4694.005587,11795.22459,32786.42962,16489.23017,0.0,0.0,0.0,0.0,0.0,,,,,,, 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+10/1/2022,69526.33142,6766.835442,14605.07557,39836.58715,21371.91102,1935.0,1341.0,28.0,23.0,0.0,0.0,7420.0,4926.0,287.0,270.0,24.0,13.0 +11/1/2022,70230.95299,6784.267513,14704.34784,40064.15138,21488.61535,0.0,0.0,0.0,0.0,0.0,,3840.0,2518.0,154.0,151.0,0.0,0.0 +12/1/2022,70966.0361,6804.717609,14810.06979,40309.35269,21614.7874,0.0,0.0,0.0,0.0,0.0,,5001.0,3376.0,211.0,207.0,0.0,0.0 diff --git a/reports/figures/results/ME/results.csv b/reports/figures/results/ME/results.csv new file mode 100644 index 0000000..83213bf --- /dev/null +++ b/reports/figures/results/ME/results.csv @@ -0,0 +1,38 @@ + Dynamic Factor Results +Dep. Variable: ,"GDP", and 16 more , No. Observations: ,131 +Model: ,Dynamic Factor Model , Log Likelihood ,-838.781 + ,+ 2 factors in 2 blocks, AIC ,1787.562 + ,+ AR(1) idiosyncratic , BIC ,1945.698 +Date: ,Sat, 26 Oct 2024 , HQIC ,1851.820 +Time: ,18:48:36 , EM Iterations ,376 +Sample: ,02-01-2012 , , + ,- 12-01-2022 , , +Covariance Type:,Not computed , , + Observation equation: +Factor loadings:,Pandemic,Consumption, idiosyncratic: AR(1),var. +GDP ,. ,-0.06 ,0.53 ,0.73 +Cons3 ,. ,-0.48 ,0.94 ,0.04 +Cons4 ,. ,-0.52 ,0.95 ,0.02 +Cons5 ,. ,-0.41 ,0.89 ,0.15 +Cons2 ,. ,-0.56 ,0.96 ,0.00 +Cases5 ,0.29 ,. ,-0.53 ,0.04 +Cases2 ,0.29 ,. ,-0.49 ,0.04 +Deaths5 ,0.29 ,. ,0.14 ,0.24 +Deaths2 ,0.29 ,. ,0.21 ,0.24 +Cases3 ,0.28 ,. ,-0.29 ,0.19 +Deaths3 ,0.29 ,. ,0.12 ,0.26 +Cases4 ,0.30 ,. ,0.42 ,0.00 +Cases1 ,0.31 ,. ,0.52 ,0.00 +Hosp2 ,0.29 ,. ,0.62 ,0.13 +Hosp1 ,0.29 ,. ,0.62 ,0.13 +Deaths4 ,0.26 ,. ,0.36 ,0.35 +Deaths1 ,0.25 ,. ,0.38 ,0.37 + Transition: Factor block 0 + ,L1.Pandemic, error variance +Pandemic,0.84 ,3.21 + Transition: Factor block 1 + ,L1.Consumption, error variance +Consumption,0.59 ,1.86 + +Warnings: +[1] Covariance matrix not calculated. diff --git a/reports/figures/results/ME/run-info.yaml b/reports/figures/results/ME/run-info.yaml new file mode 100644 index 0000000..7a81bae --- /dev/null +++ b/reports/figures/results/ME/run-info.yaml @@ -0,0 +1,45 @@ +diff_cols: [] +factor_map: + Cases1: !!python/tuple + - Pandemic + Cases2: !!python/tuple + - Pandemic + Cases3: !!python/tuple + - Pandemic + Cases4: !!python/tuple + - Pandemic + Cases5: !!python/tuple + - Pandemic + Cons2: !!python/tuple + - Consumption + Cons3: !!python/tuple + - Consumption + Cons4: !!python/tuple + - Consumption + Cons5: !!python/tuple + - Consumption + Deaths1: !!python/tuple + - Pandemic + Deaths2: !!python/tuple + - Pandemic + Deaths3: !!python/tuple + - Pandemic + Deaths4: !!python/tuple + - Pandemic + Deaths5: !!python/tuple + - Pandemic + GDP: !!python/tuple + - Consumption + Hosp1: !!python/tuple + - Pandemic + Hosp2: !!python/tuple + - Pandemic +global_multiplier: 0 +logdiff_cols: +- GDP +- Cons3 +- Cons4 +- Cons5 +- Cons2 +maxiter: 10000 +non_stationary_cols: null diff --git a/reports/figures/results/MI/df.csv b/reports/figures/results/MI/df.csv new file mode 100644 index 0000000..6e91332 --- /dev/null +++ b/reports/figures/results/MI/df.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,0.5128158055976773,0.3634073726980917,0.24121575109507798,0.5599584237244611,0.2654672484635687,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +3/1/2012,0.5164985464113719,0.3803726258838837,0.26698080175565064,0.5797487774035119,0.2882045226551938,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +4/1/2012,0.5442278860121776,0.41195694717291564,0.3140853098488309,0.6163861953346085,0.32991360248964396,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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+1/1/2018,1.3155579195923885,-1.2782966622337213,478325.1678,37985.94164,78314.81343,254248.5625,116300.7628,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +2/1/2018,1.3153958738886313,-0.5253238264308597,481419.4967,38045.3736,78394.14634,254468.9276,116439.5354,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +3/1/2018,1.3152418193821465,0.1714139905408889,484997.7268,38143.7598,78553.91224,254950.5228,116697.6952,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +4/1/2018,1.3150953536804209,-0.21076149723039522,485098.4986,38220.97517,78670.22262,255291.2052,116891.2287,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +5/1/2018,1.3149560953216155,-0.1804120121200521,485225.7848,38300.01325,78790.44855,255644.7352,117090.5003,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +6/1/2018,1.3148236826384805,0.3225547845608696,485715.5387,38407.4522,78969.22921,256188.3638,117376.7276,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +7/1/2018,1.3146977726863418,0.46466741051801597,485453.2653,38523.17134,79165.12171,256787.581,117688.3468,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +8/1/2018,1.3145780402315723,0.5498085492590938,485258.5513,38644.01885,79371.62449,257421.2764,118015.7048,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +9/1/2018,1.314464176797141,-0.4085682919228246,484390.5534,38710.84625,79467.29754,257695.6308,118178.2128,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +10/1/2018,1.314355889761977,-0.10143644922705486,483165.9995,38795.18441,79599.07826,258087.2137,118394.3392,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +11/1/2018,1.314252901511077,0.774243008018286,482569.2322,38929.54265,79833.57652,258811.9175,118763.2033,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +12/1/2018,1.3141549486334276,0.9090171992390579,482074.6667,39072.03347,80084.77764,259590.7919,119156.9029,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +1/1/2019,1.3140617811649449,-0.18982662211442136,482917.2822,39128.52406,80231.15734,260269.04,119359.7655,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +2/1/2019,1.3139731618738741,-1.3383231204009813,482949.0163,39119.32946,80242.80933,260509.9439,119362.2151,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +3/1/2019,1.313888865586105,-2.138197875519524,482415.7781,39064.42166,80160.56303,260445.1404,119225.0532,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +4/1/2019,1.3138086785481242,-2.384221959077692,482434.3232,38995.59726,80049.58402,260285.8141,119045.242,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +5/1/2019,1.3137323978253845,-0.7592242458781211,483598.9841,39019.67879,80129.20702,260745.4835,119148.9389,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +6/1/2019,1.3136598307340375,-0.7798079296591401,484748.8061,39042.66157,80206.53113,261197.3894,119249.2382,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +7/1/2019,1.3135907943040563,-1.2781486600951693,486433.2903,39037.22425,80225.40587,261458.5188,119262.668,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +8/1/2019,1.3135251147719536,-0.5289342942409108,488649.1967,39074.68613,80332.38901,262006.4408,119407.1054,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +9/1/2019,1.31346262710134,-0.7603908519123055,490698.7759,39098.99099,80412.29232,262465.837,119511.306,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +10/1/2019,1.3134031745297232,-1.6348505053701068,491385.4602,39073.30598,80389.30597,262588.8779,119462.627,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +11/1/2019,1.313346608140022,-0.5840651176398453,492826.7055,39107.81154,80490.0847,263115.7233,119597.9038,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +12/1/2019,1.3132927864553667,-1.9504281383329265,493280.3244,39064.17064,80429.94148,263115.9695,119494.1121,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +1/1/2020,1.312058783159967,-1.417759869182042,491115.5778,39178.54954,80305.66472,261710.9307,119484.2143,0.0,0.0,0.0,0.0,0.0,0.0,252.0,185.0,29.0,28.0,0.0,0.0,MI +2/1/2020,1.3101993546251274,-0.913977569775102,489319.9184,39321.50896,80241.04919,260503.3312,119562.5581,0.0,0.0,0.0,0.0,0.0,0.0,288.0,154.0,63.0,53.0,0.0,0.0,MI +3/1/2020,-10.202304682759348,1.3728503524018842,489164.8018,39596.61265,80445.70671,260168.8488,120042.3194,19903.0,19421.0,558.0,514.0,7951.0,292.0,27275.0,25587.0,10427.0,10399.0,2561.0,2548.0,MI +4/1/2020,-14.919262350785573,3.482742074850857,472994.3969,39996.28074,80900.75625,260636.6284,120897.037,34532.0,32213.0,3944.0,3736.0,35527.0,3699.0,33141.0,31257.0,7828.0,7754.0,3664.0,3538.0,MI +5/1/2020,-0.12328795361700884,-0.8656317153700286,453741.7802,40142.85973,80842.37251,259446.0277,120985.2322,43686.0,38520.0,1430.0,1381.0,19233.0,1740.0,19087.0,18410.0,1852.0,1834.0,344.0,334.0,MI +6/1/2020,1.2084070591427496,-2.5777427655340714,433442.7429,40188.35895,80582.05262,257611.9679,120770.4116,27082.0,23586.0,377.0,371.0,8017.0,462.0,13573.0,13143.0,784.0,778.0,44.0,44.0,MI +7/1/2020,1.3226721461219864,-2.1598031454130346,452067.6978,40257.80575,80372.02595,255945.1752,120629.8317,41363.0,37475.0,236.0,227.0,19846.0,257.0,28012.0,26737.0,1471.0,1464.0,0.0,0.0,MI +8/1/2020,0.7930962892885027,-2.2360341659535887,470528.7329,40322.14054,80153.87469,254258.4664,120476.0152,39178.0,35519.0,290.0,287.0,22451.0,303.0,28962.0,27642.0,1436.0,1432.0,106.0,106.0,MI +9/1/2020,0.8234982010465188,-1.3943151575559867,489477.8386,40435.65714,80035.16615,252892.0242,120470.8233,36796.0,33019.0,290.0,279.0,24989.0,330.0,37712.0,36798.0,1733.0,1726.0,84.0,84.0,MI +10/1/2020,-1.9506039997389344,-0.37367195986464113,491160.5522,40609.41531,80036.424,251906.7516,120645.8393,66505.0,60905.0,672.0,635.0,59392.0,616.0,87812.0,87697.0,4413.0,4409.0,680.0,658.0,MI +11/1/2020,-13.904767296898228,-0.2721656138532036,492917.2413,40789.27337,80049.86597,250960.2968,120839.1393,173167.0,163156.0,2377.0,2275.0,191536.0,1865.0,200689.0,200442.0,9116.0,9102.0,3334.0,3323.0,MI +12/1/2020,-12.19322113156528,-3.1183533788718467,492617.9224,40798.84084,79730.62862,248975.3009,120529.4695,107516.0,97938.0,3642.0,3333.0,139679.0,3454.0,188965.0,188576.0,7742.0,7730.0,2969.0,2911.0,MI 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+11/1/2021,-12.272233470633921,2.6771372415312857,498763.2826,45948.2868,85764.1709,255731.3593,131712.4577,209153.0,180423.0,1057.0,860.0,199950.0,1737.0,220260.0,201217.0,7139.0,6958.0,2935.0,2730.0,MI +12/1/2021,-14.60981519337108,3.0638565709398144,499571.6966,46354.87753,86216.1207,256115.1454,132570.9982,226015.0,198475.0,1649.0,1295.0,235272.0,3621.0,261195.0,243230.0,6832.0,6710.0,3492.0,3270.0,MI +1/1/2022,-11.70043190074432,-0.837934076371873,500505.989,46334.30011,86340.00592,256381.6346,132674.306,548194.0,473280.0,1535.0,1146.0,548140.0,3243.0,408383.0,371146.0,5614.0,5491.0,2825.0,2596.0,MI +2/1/2022,-2.8106909731123153,-1.3042142841426785,501092.5461,46282.7223,86404.3197,256472.3577,132687.042,101934.0,76138.0,2242.0,1974.0,101934.0,2242.0,451269.0,438307.0,2281.0,2235.0,796.0,732.0,MI +3/1/2022,0.2991813034393118,-3.7522923162507986,499877.5974,46066.36526,86158.5587,255644.2207,132224.924,27303.0,22201.0,1186.0,1032.0,27122.0,1157.0,262121.0,255069.0,985.0,971.0,86.0,79.0,MI +4/1/2022,0.5485108713853855,1.3278274002841162,502395.7674,46193.90769,86554.18727,256720.3014,132748.095,37419.0,31598.0,311.0,277.0,38425.0,340.0,210766.0,186441.0,1164.0,1135.0,26.0,20.0,MI +5/1/2022,-0.316506703234392,-1.4633975613550607,502860.3707,46133.4628,86596.32734,256748.7122,132729.7901,101885.0,85949.0,355.0,302.0,101885.0,355.0,172831.0,145524.0,1939.0,1882.0,206.0,185.0,MI +6/1/2022,-0.04330660859986324,-4.093644664545355,501383.7522,45896.55256,86304.77201,255789.2755,132201.3246,78600.0,66093.0,561.0,441.0,78600.0,561.0,106409.0,91574.0,1438.0,1406.0,137.0,114.0,MI +7/1/2022,-0.03722891111384996,3.2544562007308713,504140.3492,46155.31577,86943.92219,257589.1027,133099.238,65881.0,54890.0,510.0,381.0,65881.0,510.0,117164.0,99291.0,1597.0,1541.0,122.0,110.0,MI +8/1/2022,-0.48569220777142164,0.7652281377115839,505074.8685,46247.25204,87268.55145,258457.2611,133515.8035,98047.0,81248.0,610.0,500.0,98047.0,610.0,119255.0,99085.0,1702.0,1659.0,216.0,174.0,MI +9/1/2022,-0.6194961873871057,0.22438040088747974,505617.2173,46303.24033,87524.42531,259122.3601,133827.6656,65808.0,51457.0,586.0,470.0,65808.0,586.0,86801.0,68452.0,1211.0,1172.0,246.0,205.0,MI +10/1/2022,-0.6371873815324207,-0.02588902485599265,506131.6129,46342.85004,87748.26209,259693.2749,134091.1121,12880.0,10118.0,143.0,120.0,0.0,0.0,69797.0,54914.0,1217.0,1177.0,269.0,214.0,MI +11/1/2022,-0.4834588208650601,1.8640489795844974,508055.5377,46511.55567,88215.83269,260985.9592,134727.3884,0.0,0.0,0.0,0.0,0.0,,296807.0,185647.0,1097.0,1058.0,231.0,204.0,MI +12/1/2022,-0.34464985148452953,2.1487472571446418,510204.5526,46700.87894,88722.28766,262393.8178,135423.1666,0.0,0.0,0.0,0.0,0.0,,53413.0,41704.0,1197.0,1173.0,215.0,184.0,MI diff --git a/reports/figures/results/MI/model.csv b/reports/figures/results/MI/model.csv new file mode 100644 index 0000000..e36e891 --- /dev/null +++ b/reports/figures/results/MI/model.csv @@ -0,0 +1,29 @@ + Model Specification: Dynamic Factor Model +Model: ,Dynamic Factor Model , # of observed variables: ,17 + ,+ 2 factors in 2 blocks, # of factor blocks: ,2 + ,+ AR(1) idiosyncratic , Idiosyncratic disturbances:,AR(1) +Sample:,2012-02-01 00:00:00 , Standardize variables: ,True + ,- 2022-12-01 00:00:00 , , +Observed variables / factor loadings +Dep. variable,Pandemic,Consumption +GDP , ,X +Cons3 , ,X +Cons4 , ,X +Cons5 , ,X +Cons2 , ,X +Cases5 ,X , +Cases2 ,X , +Deaths5 ,X , +Deaths2 ,X , +Cases3 ,X , +Deaths3 ,X , +Cases4 ,X , +Cases1 ,X , +Hosp2 ,X , +Hosp1 ,X , +Deaths4 ,X , +Deaths1 ,X , + Factor blocks: + block ,order +Pandemic ,1 +Consumption,1 diff --git a/reports/figures/results/MI/raw.csv b/reports/figures/results/MI/raw.csv new file mode 100644 index 0000000..ac16aaf --- /dev/null +++ b/reports/figures/results/MI/raw.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,418891.6905,30497.56392,73596.24625,219612.6375,104093.8102,0.0,0.0,0.0,0.0,0.0,,,,,,, +3/1/2012,418724.6202,30553.71735,73549.38086,219898.3306,104103.0982,0.0,0.0,0.0,0.0,0.0,,,,,,, +4/1/2012,419578.3683,30627.74117,73546.11847,220313.0194,104173.8596,0.0,0.0,0.0,0.0,0.0,,,,,,, +5/1/2012,421454.0436,30776.35985,73722.01449,221264.3102,104498.3743,0.0,0.0,0.0,0.0,0.0,,,,,,, +6/1/2012,423070.6892,30906.082,73852.35296,222079.5555,104758.435,0.0,0.0,0.0,0.0,0.0,,,,,,, +7/1/2012,424063.1976,31009.17385,73919.03115,222703.4289,104928.205,0.0,0.0,0.0,0.0,0.0,,,,,,, +8/1/2012,423785.0279,31019.23557,73764.60988,222659.6053,104783.8454,0.0,0.0,0.0,0.0,0.0,,,,,,, +9/1/2012,423639.5063,31038.8129,73633.95232,222684.8245,104672.7652,0.0,0.0,0.0,0.0,0.0,,,,,,, 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+8/1/2013,430736.0194,31864.67671,74103.40525,227158.7693,105968.082,0.0,0.0,0.0,0.0,0.0,,,,,,, +9/1/2013,431989.0694,31956.45002,74201.00935,227679.0251,106157.4594,0.0,0.0,0.0,0.0,0.0,,,,,,, +10/1/2013,432464.7241,32006.56364,74202.12278,227902.748,106208.6864,0.0,0.0,0.0,0.0,0.0,,,,,,, +11/1/2013,432994.0159,32060.59848,74212.70077,228154.8244,106273.2992,0.0,0.0,0.0,0.0,0.0,,,,,,, +12/1/2013,433334.8027,32100.62302,74191.25426,228307.6682,106291.8773,0.0,0.0,0.0,0.0,0.0,,,,,,, +1/1/2014,431865.1779,32215.38356,74242.31832,228631.7683,106457.7019,0.0,0.0,0.0,0.0,0.0,,,,,,, +2/1/2014,430964.6791,32372.04167,74390.46754,229254.4314,106762.5092,0.0,0.0,0.0,0.0,0.0,,,,,,, +3/1/2014,429457.1058,32482.51953,74433.04272,229551.3102,106915.5623,0.0,0.0,0.0,0.0,0.0,,,,,,, +4/1/2014,431772.3359,32594.45991,74479.79729,229860.4271,107074.2572,0.0,0.0,0.0,0.0,0.0,,,,,,, +5/1/2014,434171.4034,32712.95873,74542.30187,230217.5497,107255.2606,0.0,0.0,0.0,0.0,0.0,,,,,,, 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Variable: ,"GDP", and 16 more , No. Observations: ,131 +Model: ,Dynamic Factor Model , Log Likelihood ,-1148.436 + ,+ 2 factors in 2 blocks, AIC ,2406.872 + ,+ AR(1) idiosyncratic , BIC ,2565.008 +Date: ,Sat, 26 Oct 2024 , HQIC ,2471.129 +Time: ,18:48:37 , EM Iterations ,250 +Sample: ,02-01-2012 , , + ,- 12-01-2022 , , +Covariance Type:,Not computed , , + Observation equation: +Factor loadings:,Pandemic,Consumption, idiosyncratic: AR(1),var. +GDP ,. ,-0.02 ,0.47 ,0.77 +Cons3 ,. ,0.49 ,0.94 ,0.03 +Cons4 ,. ,0.53 ,0.94 ,0.01 +Cons5 ,. ,0.40 ,0.90 ,0.13 +Cons2 ,. ,0.55 ,0.95 ,0.00 +Cases5 ,-0.28 ,. ,0.29 ,0.49 +Cases2 ,-0.28 ,. ,0.27 ,0.48 +Deaths5 ,-0.29 ,. ,0.35 ,0.27 +Deaths2 ,-0.28 ,. ,0.37 ,0.28 +Cases3 ,-0.28 ,. ,0.32 ,0.45 +Deaths3 ,-0.30 ,. ,0.20 ,0.25 +Cases4 ,-0.27 ,. ,0.59 ,0.44 +Cases1 ,-0.28 ,. ,0.61 ,0.41 +Hosp2 ,-0.30 ,. ,0.41 ,0.12 +Hosp1 ,-0.30 ,. ,0.41 ,0.12 +Deaths4 ,-0.30 ,. ,1.00 ,0.00 +Deaths1 ,-0.30 ,. ,0.97 ,0.00 + Transition: Factor block 0 + ,L1.Pandemic, error variance +Pandemic,0.71 ,5.95 + Transition: Factor block 1 + ,L1.Consumption, error variance +Consumption,0.57 ,2.15 + +Warnings: +[1] Covariance matrix not calculated. diff --git a/reports/figures/results/MI/run-info.yaml b/reports/figures/results/MI/run-info.yaml new file mode 100644 index 0000000..7a81bae --- /dev/null +++ b/reports/figures/results/MI/run-info.yaml @@ -0,0 +1,45 @@ +diff_cols: [] +factor_map: + Cases1: !!python/tuple + - Pandemic + Cases2: !!python/tuple + - Pandemic + Cases3: !!python/tuple + - Pandemic + Cases4: !!python/tuple + - Pandemic + Cases5: !!python/tuple + - Pandemic + Cons2: !!python/tuple + - Consumption + Cons3: !!python/tuple + - Consumption + Cons4: !!python/tuple + - Consumption + Cons5: !!python/tuple + - Consumption + Deaths1: !!python/tuple + - Pandemic + Deaths2: !!python/tuple + - Pandemic + Deaths3: !!python/tuple + - Pandemic + Deaths4: !!python/tuple + - Pandemic + Deaths5: !!python/tuple + - Pandemic + GDP: !!python/tuple + - Consumption + Hosp1: !!python/tuple + - Pandemic + Hosp2: !!python/tuple + - Pandemic +global_multiplier: 0 +logdiff_cols: +- GDP +- Cons3 +- Cons4 +- Cons5 +- Cons2 +maxiter: 10000 +non_stationary_cols: null diff --git a/reports/figures/results/MN/df.csv b/reports/figures/results/MN/df.csv new file mode 100644 index 0000000..08a7c61 --- /dev/null +++ b/reports/figures/results/MN/df.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,0.6530343363770293,0.3435259515518511,0.28567642838885793,0.5999385878389446,0.23729721068096354,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +3/1/2012,0.6585305216983678,0.36474023602738864,0.309120129005488,0.6167105707298939,0.26617188964664623,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +4/1/2012,0.6035916106529987,0.40373388068918326,0.35208057957691075,0.6476028702387966,0.3191355752110573,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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+8/1/2018,-1.1718619866751558,-1.225312539792173,344622.9563,28116.80891,42644.7746,161214.3555,70761.52206,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +9/1/2018,-1.1718619866751547,-0.20513690414186514,345170.4096,28180.32794,42703.86158,161476.8756,70884.12055,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +10/1/2018,-1.171861986675155,-0.5272190648414683,345115.5852,28256.53667,42782.32223,161812.5023,71038.78239,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +11/1/2018,-1.1718619866751532,-1.4523061822910759,345507.4379,28369.14627,42915.97457,162356.7941,71285.03672,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +12/1/2018,-1.171861986675154,-1.5915798269607293,345971.961,28487.67329,43058.60117,162935.013,71546.18271,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +1/1/2019,-1.1718619866751534,-0.023760251343410088,345641.3127,28554.89385,43098.47491,163272.3648,71653.28464,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +2/1/2019,-1.1718619866751543,1.1952598545051707,344731.6133,28574.14228,43066.03082,163335.4864,71640.09675,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +3/1/2019,-1.1718619866751532,2.0443497866616056,343422.6035,28559.88962,42983.35663,163207.3088,71543.17773,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +4/1/2019,-1.1718619866751534,2.305790498638025,343834.7908,28535.31305,42885.49827,163020.4044,71420.75059,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +5/1/2019,-1.1718619866751543,0.5829817513165556,345062.324,28578.62414,42889.89677,163221.5127,71468.46781,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +6/1/2019,-1.1718619866751543,0.6055684041004695,346278.7101,28621.09428,42893.11866,163417.8833,71514.16746,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +7/1/2019,-1.1718619866751538,1.1347473658608387,347868.3973,28642.67505,42865.16522,163495.0815,71507.80242,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +8/1/2019,-1.171861986675154,0.34079130172413974,349837.4375,28695.68639,42884.35695,163751.7706,71580.01308,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +9/1/2019,-1.1718619866751543,0.5869461823839492,351687.0072,28739.00932,42889.13136,163953.2198,71628.118,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +10/1/2019,-1.171861986675154,1.5150140001057526,352250.437,28745.52101,42839.12772,163944.7851,71584.63364,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +11/1/2019,-1.1718619866751545,0.40113033879951454,353354.6063,28796.25355,42855.16751,164188.6638,71651.41352,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +12/1/2019,-1.1718619866751532,1.8508158889400264,353750.4515,28789.37292,42785.6343,164104.1757,71575.00722,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +1/1/2020,-1.171861986675154,0.9861317303098502,351659.6661,28863.18773,42721.44055,162777.9856,71584.62828,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,MN +2/1/2020,-1.171828268928335,0.45345958435127054,349834.7889,28958.08949,42688.97992,161575.9581,71647.06942,0.0,0.0,0.0,0.0,0.0,0.0,32.0,32.0,8.0,8.0,0.0,0.0,MN +3/1/2020,-1.1074042433183546,-1.9701191462125598,349182.2168,29150.29687,42799.77712,160914.9868,71950.07399,689.0,689.0,17.0,17.0,629.0,12.0,2496.0,2486.0,644.0,644.0,56.0,56.0,MN +4/1/2020,-0.7413689057190762,-4.205125062980562,341846.7181,29434.12829,43043.80562,160746.785,72477.93391,4447.0,4447.0,326.0,326.0,4507.0,331.0,16598.0,16580.0,2500.0,2494.0,966.0,966.0,MN +5/1/2020,0.6906451089990373,0.4089479464891266,332302.1878,29531.66203,43014.66657,159553.8333,72546.3286,20072.0,20072.0,717.0,707.0,19714.0,707.0,36446.0,36424.0,3922.0,3920.0,986.0,986.0,MN +6/1/2020,-0.11399691300052694,2.226508821542537,321970.3646,29554.88234,42878.07235,157967.2855,72432.95469,11508.0,11508.0,422.0,395.0,11453.0,426.0,23430.0,23430.0,1686.0,1686.0,25.0,25.0,MN +7/1/2020,0.5309734525443854,1.783343138563086,329122.4911,29595.78021,42768.22692,156486.2957,72364.00713,18472.0,18472.0,164.0,161.0,18160.0,164.0,40386.0,40382.0,2338.0,2338.0,0.0,0.0,MN +8/1/2020,0.7634876767531077,1.8651201357467344,336193.5237,29632.98059,42654.04673,154995.8861,72287.02731,20676.0,20676.0,220.0,211.0,21401.0,226.0,41780.0,41772.0,2582.0,2580.0,138.0,138.0,MN +9/1/2020,0.9928922731816461,0.9733793780953364,343642.9448,29706.37381,42592.77593,153702.9513,72299.14974,23270.0,23270.0,223.0,219.0,23270.0,223.0,52572.0,51884.0,2891.0,2889.0,266.0,256.0,MN +10/1/2020,3.4184132741725373,-0.10750107732876923,345657.2143,29824.04521,42595.3443,152642.6656,72419.38951,49338.0,48697.0,422.0,402.0,49338.0,422.0,124325.0,121061.0,6547.0,6501.0,861.0,809.0,MN +11/1/2020,14.618970938229396,-0.21375782431935275,347723.1506,29946.20117,42604.39615,151606.1215,72550.59732,170291.0,164262.0,1141.0,1083.0,170291.0,1141.0,366503.0,350488.0,14735.0,14441.0,3132.0,2906.0,MN +12/1/2020,7.712050864855097,2.8060658742921647,348334.9978,29943.3794,42436.37985,149944.2711,72379.75925,96539.0,88865.0,1730.0,1630.0,96539.0,1730.0,157830.0,145014.0,8282.0,7774.0,1556.0,1396.0,MN +1/1/2021,3.1022550459496916,-4.618314473102906,349315.0535,30234.84896,42718.2834,150710.3426,72953.13236,46505.0,40820.0,886.0,801.0,46505.0,886.0,76766.0,66562.0,4009.0,3735.0,243.0,227.0,MN +2/1/2021,0.9265461351287823,-4.880533535757382,350458.1522,30538.94707,43018.9623,151544.294,73557.90937,22787.0,19225.0,283.0,260.0,22787.0,283.0,44225.0,37332.0,2375.0,2217.0,0.0,0.0,MN +3/1/2021,1.9998847544067293,-2.919871087896867,350687.0424,30761.49324,43205.94111,151979.79,73967.43435,34044.0,27932.0,237.0,123.0,34935.0,375.0,82169.0,68447.0,3986.0,3648.0,22.0,22.0,MN +4/1/2021,4.025888653492563,-3.2612264818074115,352789.9356,30999.05461,43415.41344,152496.8925,74414.46804,56283.0,47210.0,311.0,278.0,56283.0,311.0,109725.0,91789.0,5864.0,5370.0,24.0,24.0,MN +5/1/2021,1.1869537081097306,-3.3454183978030607,354947.9035,31240.90961,43632.17325,153041.8528,74873.08285,25638.0,21035.0,282.0,265.0,25572.0,282.0,39451.0,32889.0,2804.0,2640.0,0.0,0.0,MN +6/1/2021,-0.8047664581238809,-2.908233085768065,356904.7619,31464.51523,43824.72414,153504.1747,75289.23938,3915.0,3263.0,168.0,158.0,3981.0,168.0,8004.0,6722.0,1006.0,964.0,0.0,0.0,MN +7/1/2021,-0.4773039697053704,-3.533402980788915,356240.6095,31715.98296,44057.27846,154108.7502,75773.26143,7543.0,6194.0,76.0,73.0,7336.0,74.0,22234.0,18757.0,1760.0,1645.0,0.0,0.0,MN +8/1/2021,2.26126665013728,-3.895757556452589,355796.234,31984.38734,44314.34853,154800.8485,76298.73587,37056.0,32739.0,136.0,130.0,37263.0,143.0,83623.0,73762.0,4625.0,4425.0,32.0,32.0,MN +9/1/2021,4.469581956692372,-4.403476978708225,355641.3088,32276.64201,44605.27104,155612.6507,76881.91305,61130.0,51189.0,296.0,250.0,61130.0,352.0,138105.0,116361.0,6064.0,5632.0,209.0,195.0,MN +10/1/2021,5.882209073236569,-2.4310398049795134,356688.4862,32481.9755,44777.06654,156010.6487,77259.04204,76456.0,64352.0,537.0,478.0,76456.0,537.0,156649.0,130449.0,6848.0,6426.0,469.0,419.0,MN +11/1/2021,9.83842665838088,-2.0661978684564097,357572.4478,32671.15059,44927.89763,156337.9653,77599.04822,116866.0,93663.0,713.0,661.0,124820.0,713.0,246831.0,196352.0,10390.0,9565.0,1044.0,943.0,MN +12/1/2021,9.03851218505931,-2.500345211905098,358686.9851,32880.15079,45107.24413,156766.7724,77987.39492,109842.0,86499.0,1150.0,1045.0,109842.0,1150.0,268807.0,212654.0,9325.0,8702.0,540.0,494.0,MN +1/1/2022,26.38915678235739,-0.30091358252145994,358706.4148,32867.43841,45272.38586,156996.0617,78139.82428,299541.0,240471.0,905.0,1539.0,299541.0,905.0,608613.0,489238.0,12833.0,12363.0,800.0,697.0,MN +2/1/2022,7.280109762214207,0.20554265605602118,358483.8601,32832.71481,45405.17677,157116.9848,78237.89157,91823.0,74930.0,728.0,698.0,91823.0,728.0,92503.0,77119.0,4088.0,3990.0,37.0,29.0,MN +3/1/2022,0.31744129883166927,2.8134122023654697,356982.3031,32681.06923,45373.51324,156674.0094,78054.58247,15974.0,13353.0,349.0,299.0,15974.0,349.0,25862.0,21677.0,1631.0,1611.0,0.0,0.0,MN +4/1/2022,0.9531471573952233,-2.5632194676252587,359419.9715,32773.37677,45678.50963,157397.3166,78451.8864,22997.0,19111.0,140.0,100.0,22997.0,140.0,56012.0,46775.0,2102.0,2052.0,0.0,0.0,MN +5/1/2022,3.861499559637124,0.40845776811608214,360383.4015,32732.29763,45796.18425,157477.78,78528.48188,54621.0,45245.0,165.0,125.0,54621.0,165.0,115062.0,94381.0,4322.0,4155.0,34.0,28.0,MN +6/1/2022,3.034064831977589,3.20875493354945,359945.6733,32565.98549,45735.87991,156951.3489,78301.86541,45672.0,37328.0,184.0,173.0,45672.0,184.0,85563.0,69933.0,3873.0,3759.0,11.0,8.0,MN +7/1/2022,2.3097030593697516,-4.573788116727272,363169.1208,32751.36349,46167.95114,158117.438,78919.31462,37724.0,30444.0,166.0,147.0,37724.0,166.0,83071.0,66893.0,4237.0,4122.0,0.0,0.0,MN +8/1/2022,2.5347646949752436,-1.9227486883768181,365077.832,32818.35963,46432.84732,158711.5285,79251.20695,40255.0,32712.0,136.0,125.0,40255.0,136.0,73590.0,60023.0,3903.0,3810.0,26.0,16.0,MN +9/1/2022,1.9428139068359824,-1.338609789655672,366695.5555,32859.83537,46660.59893,159180.554,79520.4343,33978.0,27981.0,222.0,207.0,33978.0,222.0,26597.0,22073.0,1944.0,1887.0,19.0,15.0,MN +10/1/2022,-0.7905311901487764,-1.0628162603813212,366365.2777,32889.67526,46870.61511,159591.2873,79760.29037,5968.0,5025.0,44.0,40.0,0.0,0.0,23194.0,19166.0,2043.0,1970.0,33.0,20.0,MN +11/1/2022,-1.2091971778076225,-3.0570636117941308,367058.616,33011.12679,47210.39355,160445.292,80221.52034,0.0,0.0,0.0,0.0,0.0,,23433.0,20035.0,2230.0,2173.0,85.0,53.0,MN +12/1/2022,-1.1622831146863413,-3.348836367079479,367915.6151,33147.20894,47570.84826,161370.0134,80718.05719,0.0,0.0,0.0,0.0,0.0,,24060.0,20362.0,2422.0,2372.0,70.0,37.0,MN diff --git a/reports/figures/results/MN/model.csv b/reports/figures/results/MN/model.csv new file mode 100644 index 0000000..e36e891 --- /dev/null +++ b/reports/figures/results/MN/model.csv @@ -0,0 +1,29 @@ + Model Specification: Dynamic Factor Model +Model: ,Dynamic Factor Model , # of observed variables: ,17 + ,+ 2 factors in 2 blocks, # of factor blocks: ,2 + ,+ AR(1) idiosyncratic , Idiosyncratic disturbances:,AR(1) +Sample:,2012-02-01 00:00:00 , Standardize variables: ,True + ,- 2022-12-01 00:00:00 , , +Observed variables / factor loadings +Dep. variable,Pandemic,Consumption +GDP , ,X +Cons3 , ,X +Cons4 , ,X +Cons5 , ,X +Cons2 , ,X +Cases5 ,X , +Cases2 ,X , +Deaths5 ,X , +Deaths2 ,X , +Cases3 ,X , +Deaths3 ,X , +Cases4 ,X , +Cases1 ,X , +Hosp2 ,X , +Hosp1 ,X , +Deaths4 ,X , +Deaths1 ,X , + Factor blocks: + block ,order +Pandemic ,1 +Consumption,1 diff --git a/reports/figures/results/MN/raw.csv b/reports/figures/results/MN/raw.csv new file mode 100644 index 0000000..043f0d4 --- /dev/null +++ b/reports/figures/results/MN/raw.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,294888.4089,22717.46446,43092.70514,135937.3315,65810.18635,0.0,0.0,0.0,0.0,0.0,,,,,,, +3/1/2012,295976.4376,22726.87622,43088.33409,136033.213,65815.23537,0.0,0.0,0.0,0.0,0.0,,,,,,, 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+12/1/2022,367915.6151,33147.20894,47570.84826,161370.0134,80718.05719,0.0,0.0,0.0,0.0,0.0,,24060.0,20362.0,2422.0,2372.0,70.0,37.0 diff --git a/reports/figures/results/MN/results.csv b/reports/figures/results/MN/results.csv new file mode 100644 index 0000000..ed8c265 --- /dev/null +++ b/reports/figures/results/MN/results.csv @@ -0,0 +1,38 @@ + Dynamic Factor Results +Dep. Variable: ,"GDP", and 16 more , No. Observations: ,131 +Model: ,Dynamic Factor Model , Log Likelihood ,-523.535 + ,+ 2 factors in 2 blocks, AIC ,1157.070 + ,+ AR(1) idiosyncratic , BIC ,1315.206 +Date: ,Sat, 26 Oct 2024 , HQIC ,1221.328 +Time: ,18:48:39 , EM Iterations ,370 +Sample: ,02-01-2012 , , + ,- 12-01-2022 , , +Covariance Type:,Not computed , , + Observation equation: +Factor loadings:,Pandemic,Consumption, idiosyncratic: AR(1),var. +GDP ,. ,-0.07 ,0.53 ,0.71 +Cons3 ,. ,-0.48 ,0.93 ,0.05 +Cons4 ,. ,-0.52 ,0.96 ,0.02 +Cons5 ,. ,-0.40 ,0.94 ,0.08 +Cons2 ,. ,-0.56 ,0.97 ,0.00 +Cases5 ,0.30 ,. ,0.17 ,0.00 +Cases2 ,0.30 ,. ,0.66 ,0.00 +Deaths5 ,0.28 ,. ,0.14 ,0.40 +Deaths2 ,0.29 ,. ,0.26 ,0.24 +Cases3 ,0.30 ,. ,-0.22 ,0.00 +Deaths3 ,0.28 ,. ,0.13 ,0.40 +Cases4 ,0.29 ,. ,0.11 ,0.02 +Cases1 ,0.30 ,. ,0.27 ,0.03 +Hosp2 ,0.30 ,. ,0.43 ,0.14 +Hosp1 ,0.30 ,. ,0.45 ,0.14 +Deaths4 ,0.25 ,. ,0.57 ,0.41 +Deaths1 ,0.25 ,. ,0.56 ,0.43 + Transition: Factor block 0 + ,L1.Pandemic, error variance +Pandemic,0.67 ,6.34 + Transition: Factor block 1 + ,L1.Consumption, error variance +Consumption,0.53 ,2.16 + +Warnings: +[1] Covariance matrix not calculated. diff --git a/reports/figures/results/MN/run-info.yaml b/reports/figures/results/MN/run-info.yaml new file mode 100644 index 0000000..7a81bae --- /dev/null +++ b/reports/figures/results/MN/run-info.yaml @@ -0,0 +1,45 @@ +diff_cols: [] +factor_map: + Cases1: !!python/tuple + - Pandemic + Cases2: !!python/tuple + - Pandemic + Cases3: !!python/tuple + - Pandemic + Cases4: !!python/tuple + - Pandemic + Cases5: !!python/tuple + - Pandemic + Cons2: !!python/tuple + - Consumption + Cons3: !!python/tuple + - Consumption + Cons4: !!python/tuple + - Consumption + Cons5: !!python/tuple + - Consumption + Deaths1: !!python/tuple + - Pandemic + Deaths2: !!python/tuple + - Pandemic + Deaths3: !!python/tuple + - Pandemic + Deaths4: !!python/tuple + - Pandemic + Deaths5: !!python/tuple + - Pandemic + GDP: !!python/tuple + - Consumption + Hosp1: !!python/tuple + - Pandemic + Hosp2: !!python/tuple + - Pandemic +global_multiplier: 0 +logdiff_cols: +- GDP +- Cons3 +- Cons4 +- Cons5 +- Cons2 +maxiter: 10000 +non_stationary_cols: null diff --git a/reports/figures/results/NJ/df.csv b/reports/figures/results/NJ/df.csv new file mode 100644 index 0000000..21197eb --- /dev/null +++ b/reports/figures/results/NJ/df.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,0.6307725525817746,0.23334243707787863,0.27506782853157624,0.5174705981616783,0.25807571543113444,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +3/1/2012,0.6349484777643203,0.24821147742570066,0.29814989141680903,0.5375867380606666,0.27765403929953314,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +4/1/2012,0.5792144507104009,0.27543458825565753,0.34043739455424976,0.5747036346031699,0.3135147510104363,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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+10/1/2019,1.2262999759087947,0.27516836465960615,587060.4554,40110.00124,86088.42059,290593.3546,126198.4369,0.0,0.0,0.0,0.0,0.0,,,,,,,,NJ +11/1/2019,1.2262999759087951,-0.6497442053372833,587757.5911,40210.81329,86314.13187,291355.1313,126524.9527,0.0,0.0,0.0,0.0,0.0,,,,,,,,NJ +12/1/2019,1.2262999759087951,0.5612557283803477,587279.7464,40231.09016,86366.94481,291533.2864,126598.035,0.0,0.0,0.0,0.0,0.0,,,,,,,,NJ +1/1/2020,0.9564229083376176,1.2432692218637165,585685.7514,40228.68895,86299.49194,290077.8321,126528.1809,0.0,0.0,0.0,0.0,0.0,0.0,745.0,663.0,696.0,655.0,89.0,76.0,NJ +2/1/2020,1.186368026551051,0.7967409588251221,584528.9457,40255.99472,86295.9545,288840.9111,126551.9492,0.0,0.0,0.0,0.0,0.0,0.0,194.0,170.0,103.0,101.0,0.0,0.0,NJ +3/1/2020,-10.304330804599758,-1.225465716524456,585332.9604,40418.45629,86582.0806,288572.1956,127000.5369,18696.0,18696.0,267.0,267.0,18696.0,366.0,76659.0,75461.0,29705.0,29496.0,6026.0,5783.0,NJ 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+9/1/2020,0.034857153766039484,1.2120759956516431,572806.1247,40568.25818,86533.44592,281111.6537,127101.7041,15118.0,13315.0,177.0,170.0,15161.0,177.0,34033.0,30613.0,3071.0,2896.0,0.0,0.0,NJ +10/1/2020,-1.8248771070446543,0.30909988963742513,576086.4204,40628.105,86600.19287,280120.3783,127228.2979,39343.0,32611.0,228.0,222.0,39429.0,228.0,86691.0,73754.0,7868.0,7356.0,47.0,43.0,NJ +11/1/2020,-7.409145722000547,0.21820872246320366,579452.8624,40694.10862,86680.09212,279172.2189,127374.2007,117852.0,99418.0,643.0,607.0,117996.0,643.0,262942.0,220688.0,22273.0,20586.0,1804.0,1693.0,NJ +12/1/2020,-8.737507767346095,2.732190494664679,580396.5225,40590.72671,86399.465,277068.3014,126990.1917,160001.0,140056.0,2049.0,1857.0,160941.0,2049.0,319590.0,269941.0,25704.0,23430.0,3069.0,2824.0,NJ +1/1/2021,-7.03588291326346,-5.997992286231005,583067.485,41242.72759,87209.52542,278564.1704,128452.253,169045.0,146181.0,2442.0,2334.0,167802.0,2442.0,329585.0,273844.0,21315.0,19313.0,2675.0,2362.0,NJ 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+10/1/2022,-0.6787177214641699,0.17407401492293162,614054.923,47227.11698,95290.02137,298336.6099,142517.1384,14106.0,10347.0,34.0,32.0,0.0,0.0,57847.0,41192.0,4914.0,4583.0,33.0,31.0,NJ +11/1/2022,-0.696857646091958,-1.5008382939638016,617228.5908,47374.96393,95712.49252,300168.2379,143087.4564,0.0,0.0,0.0,0.0,0.0,,55792.0,40398.0,4961.0,4649.0,78.0,76.0,NJ +12/1/2022,-1.2889997591311653,-1.7573132665865434,620674.4706,47543.847,96177.27888,302131.8174,143721.1259,0.0,0.0,0.0,0.0,0.0,,83910.0,60007.0,6488.0,6105.0,85.0,78.0,NJ diff --git a/reports/figures/results/NJ/model.csv b/reports/figures/results/NJ/model.csv new file mode 100644 index 0000000..e36e891 --- /dev/null +++ b/reports/figures/results/NJ/model.csv @@ -0,0 +1,29 @@ + Model Specification: Dynamic Factor Model +Model: ,Dynamic Factor Model , # of observed variables: ,17 + ,+ 2 factors in 2 blocks, # of factor blocks: ,2 + ,+ AR(1) idiosyncratic , Idiosyncratic disturbances:,AR(1) +Sample:,2012-02-01 00:00:00 , Standardize variables: ,True + ,- 2022-12-01 00:00:00 , , +Observed variables / factor loadings +Dep. variable,Pandemic,Consumption +GDP , ,X +Cons3 , ,X +Cons4 , ,X +Cons5 , ,X +Cons2 , ,X +Cases5 ,X , +Cases2 ,X , +Deaths5 ,X , +Deaths2 ,X , +Cases3 ,X , +Deaths3 ,X , +Cases4 ,X , +Cases1 ,X , +Hosp2 ,X , +Hosp1 ,X , +Deaths4 ,X , +Deaths1 ,X , + Factor blocks: + block ,order +Pandemic ,1 +Consumption,1 diff --git a/reports/figures/results/NJ/raw.csv b/reports/figures/results/NJ/raw.csv new file mode 100644 index 0000000..898212f --- /dev/null +++ b/reports/figures/results/NJ/raw.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,512729.8542,37244.19442,80660.45507,247644.6163,117904.7499,0.0,0.0,0.0,0.0,0.0,,,,,,, +3/1/2012,514618.8399,37233.56896,80648.39324,247874.0663,117882.0625,0.0,0.0,0.0,0.0,0.0,,,,,,, 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+11/1/2022,617228.5908,47374.96393,95712.49252,300168.2379,143087.4564,0.0,0.0,0.0,0.0,0.0,,55792.0,40398.0,4961.0,4649.0,78.0,76.0 +12/1/2022,620674.4706,47543.847,96177.27888,302131.8174,143721.1259,0.0,0.0,0.0,0.0,0.0,,83910.0,60007.0,6488.0,6105.0,85.0,78.0 diff --git a/reports/figures/results/NJ/results.csv b/reports/figures/results/NJ/results.csv new file mode 100644 index 0000000..033a191 --- /dev/null +++ b/reports/figures/results/NJ/results.csv @@ -0,0 +1,38 @@ + Dynamic Factor Results +Dep. Variable: ,"GDP", and 16 more , No. Observations: ,131 +Model: ,Dynamic Factor Model , Log Likelihood ,-1245.263 + ,+ 2 factors in 2 blocks, AIC ,2600.526 + ,+ AR(1) idiosyncratic , BIC ,2758.662 +Date: ,Sat, 26 Oct 2024 , HQIC ,2664.783 +Time: ,18:48:40 , EM Iterations ,342 +Sample: ,02-01-2012 , , + ,- 12-01-2022 , , +Covariance Type:,Not computed , , + Observation equation: +Factor loadings:,Pandemic,Consumption, idiosyncratic: AR(1),var. +GDP ,. ,-0.09 ,0.51 ,0.74 +Cons3 ,. ,-0.51 ,0.77 ,0.02 +Cons4 ,. ,-0.53 ,0.77 ,0.01 +Cons5 ,. ,-0.40 ,0.88 ,0.15 +Cons2 ,. ,-0.53 ,0.74 ,0.00 +Cases5 ,-0.29 ,. ,0.39 ,0.35 +Cases2 ,-0.29 ,. ,0.34 ,0.36 +Deaths5 ,-0.28 ,. ,0.29 ,0.42 +Deaths2 ,-0.28 ,. ,0.30 ,0.43 +Cases3 ,-0.29 ,. ,0.38 ,0.36 +Deaths3 ,-0.28 ,. ,0.29 ,0.43 +Cases4 ,-0.28 ,. ,0.51 ,0.28 +Cases1 ,-0.29 ,. ,0.49 ,0.28 +Hosp2 ,-0.33 ,. ,0.69 ,0.00 +Hosp1 ,-0.32 ,. ,0.65 ,0.00 +Deaths4 ,-0.25 ,. ,0.40 ,0.34 +Deaths1 ,-0.26 ,. ,0.44 ,0.31 + Transition: Factor block 0 + ,L1.Pandemic, error variance +Pandemic,0.68 ,5.04 + Transition: Factor block 1 + ,L1.Consumption, error variance +Consumption,0.67 ,1.97 + +Warnings: +[1] Covariance matrix not calculated. diff --git a/reports/figures/results/NJ/run-info.yaml b/reports/figures/results/NJ/run-info.yaml new file mode 100644 index 0000000..7a81bae --- /dev/null +++ b/reports/figures/results/NJ/run-info.yaml @@ -0,0 +1,45 @@ +diff_cols: [] +factor_map: + Cases1: !!python/tuple + - Pandemic + Cases2: !!python/tuple + - Pandemic + Cases3: !!python/tuple + - Pandemic + Cases4: !!python/tuple + - Pandemic + Cases5: !!python/tuple + - Pandemic + Cons2: !!python/tuple + - Consumption + Cons3: !!python/tuple + - Consumption + Cons4: !!python/tuple + - Consumption + Cons5: !!python/tuple + - Consumption + Deaths1: !!python/tuple + - Pandemic + Deaths2: !!python/tuple + - Pandemic + Deaths3: !!python/tuple + - Pandemic + Deaths4: !!python/tuple + - Pandemic + Deaths5: !!python/tuple + - Pandemic + GDP: !!python/tuple + - Consumption + Hosp1: !!python/tuple + - Pandemic + Hosp2: !!python/tuple + - Pandemic +global_multiplier: 0 +logdiff_cols: +- GDP +- Cons3 +- Cons4 +- Cons5 +- Cons2 +maxiter: 10000 +non_stationary_cols: null diff --git a/reports/figures/results/NY/df.csv b/reports/figures/results/NY/df.csv new file mode 100644 index 0000000..f6bcf66 --- /dev/null +++ b/reports/figures/results/NY/df.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,0.741389337153302,0.30488930559429755,0.3863946351630045,0.552788378192759,0.381716750574255,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +3/1/2012,0.7459036343014811,0.32243973987641755,0.4122037689566291,0.5706730084090738,0.41054932807311234,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +4/1/2012,0.7344030255926585,0.3547294140473195,0.45970860335384256,0.6035846569052918,0.46361246551291796,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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+7/1/2014,1.1271301780821665,-0.5276002404797399,1383134.025,68584.3428,163523.7345,560910.5453,232108.037,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +8/1/2014,1.1271301780821614,-1.8509051020751854,1391412.136,68822.96119,164195.541,563296.3096,233018.4699,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +9/1/2014,1.127130178082163,-1.528281072747879,1399163.396,69035.5416,164805.214,565468.9059,233840.7314,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +10/1/2014,1.1271301780821639,-1.9289525943157102,1405392.901,69282.91512,165497.9469,567926.492,234780.8459,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +11/1/2014,1.12713017808216,-2.3379793451438804,1412351.226,69566.15712,166276.4635,570678.5372,235842.6125,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +12/1/2014,1.1271301780821588,-2.9551221600454194,1420409.462,69903.4389,167184.3793,573874.8786,237087.8181,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +1/1/2015,1.1271301780821545,-3.3808034359370485,1429268.205,70333.62612,168129.8411,578209.0714,238463.4591,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +2/1/2015,1.1271301780821628,1.7650312492643518,1429185.098,70323.77185,168023.1535,578928.0843,238346.9091,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +3/1/2015,1.1271301780821654,1.9871720142120672,1428713.131,70294.8041,167871.0943,579486.9008,238165.8741,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +4/1/2015,1.1271301780821643,1.0454184281471532,1435334.143,70346.51637,167911.9295,580708.6775,238258.4135,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +5/1/2015,1.1271301780821688,2.3746463870523593,1439610.622,70284.18124,167680.8022,580986.4913,237964.9431,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +6/1/2015,1.127130178082159,2.0060289024315043,1444509.366,70253.39603,167525.3009,581521.6823,237778.6486,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +7/1/2015,1.1271301780821656,1.092922771085328,1446212.785,70300.79949,167556.4734,582701.9025,237857.2165,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +8/1/2015,1.1271301780821574,0.42797347121460017,1449089.184,70405.25125,167723.6841,584354.3242,238128.8709,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +9/1/2015,1.1271301780821619,-0.7923587483773205,1454130.353,70614.8283,168141.2106,586880.4277,238755.9663,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +10/1/2015,1.1271301780821625,0.30392811112103657,1449322.611,70730.38713,168334.7374,588626.421,239065.0438,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +11/1/2015,1.1271301780821688,0.9244138865225588,1443423.137,70792.50414,168401.1132,589927.301,239193.5286,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +12/1/2015,1.1271301780821754,-0.24125215677638018,1439577.446,70955.42018,168707.2531,592068.3176,239662.5764,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +1/1/2016,1.127130178082171,1.0621305123422804,1454594.28,71000.26459,168750.7744,593793.0598,239750.9503,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +2/1/2016,1.127130178082173,0.13377265457691906,1471261.147,71125.57551,168985.5388,596190.9074,240111.0336,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +3/1/2016,1.1271301780821705,2.538699848255208,1483591.867,71042.41358,168725.1041,596839.0091,239767.4452,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +4/1/2016,1.1271301780821645,3.3486535701475035,1484729.78,70889.23777,168298.7392,596891.376,239187.9128,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +5/1/2016,1.1271301780821672,1.8587195632759055,1488546.794,70864.96693,168178.7078,598022.7278,239043.6186,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +6/1/2016,1.127130178082164,2.203857628625108,1491722.198,70810.84713,167988.049,598897.6722,238798.8481,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +7/1/2016,1.1271301780821645,0.9828532337027687,1490203.388,70862.16712,168047.6741,600661.3141,238909.8012,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +8/1/2016,1.1271301780821654,1.8716983141983012,1487074.112,70836.67069,167925.2496,601771.2904,238761.8883,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +9/1/2016,1.1271301780821652,2.066533095076025,1483599.35,70794.31878,167763.0673,602733.8004,238557.3622,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +10/1/2016,1.1271301780821716,2.4558956275951873,1484726.458,70718.35296,167521.4723,603404.9237,238239.8094,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +11/1/2016,1.1271301780821568,0.9596555610316079,1488555.924,70771.43711,167585.7373,605173.7562,238357.1665,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +12/1/2016,1.1271301780821625,2.140141003622091,1490236.628,70722.64491,167408.8957,606068.5413,238131.5406,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +1/1/2017,1.1271301780821714,1.580704008264643,1491037.777,70646.64605,167422.128,606034.3301,238068.774,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +2/1/2017,1.1271301780821577,-0.1976204316224659,1495069.054,70724.29036,167798.4592,607314.8938,238522.7495,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +3/1/2017,1.1271301780821643,-1.897500323404438,1502213.682,70949.3233,168524.2911,609860.4898,239473.6144,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +4/1/2017,1.1271301780821603,0.3644353406528076,1498697.389,70979.40939,168786.7554,610729.3716,239766.1648,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +5/1/2017,1.1271301780821668,-1.6594954799398254,1498902.164,71185.3523,169467.0368,613110.2156,240652.3892,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +6/1/2017,1.1271301780821648,-0.6337544027636297,1497251.146,71303.02887,169937.06,614730.4257,241240.0888,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +7/1/2017,1.1271301780821639,-1.1979544738421666,1503119.933,71470.41502,170525.3271,616778.4685,241995.7421,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +8/1/2017,1.127130178082166,0.544812866345318,1505799.723,71486.46313,170752.0132,617518.9064,242238.4763,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +9/1/2017,1.127130178082175,1.6822072912628094,1506384.577,71403.73365,170741.612,617402.4096,242145.3456,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +10/1/2017,1.1271301780821572,-0.17119679448175074,1516962.843,71482.79074,171117.1017,618681.7068,242599.8925,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +11/1/2017,1.1271301780821648,-0.010150553170735654,1527224.027,71548.21749,171459.3806,619841.1733,243007.5981,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +12/1/2017,1.1271301780821592,-0.17181845095933357,1537767.433,71628.1548,171835.8572,621124.5093,243464.012,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +1/1/2018,1.1271301780821665,1.1537947171267915,1544701.788,71648.63577,171878.7839,621417.8278,243527.4197,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +2/1/2018,1.12713017808217,0.2490932818351706,1553302.547,71748.21525,172111.4983,622396.4847,243859.7135,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +3/1/2018,1.1271301780821745,-0.5879563051375518,1563469.943,71921.3051,172520.5749,624012.382,244441.88,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +4/1/2018,1.127130178082159,-0.12941161719052985,1567824.234,72054.5184,172834.0186,625281.914,244888.537,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +5/1/2018,1.1271301780821554,-0.16619594376668098,1572248.728,72191.21539,173155.8419,626581.2406,245347.0573,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +6/1/2018,1.1271301780821632,-0.7703665529537198,1577835.52,72381.48213,173606.1745,628345.1813,245987.6566,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +7/1/2018,1.127130178082174,-0.9411600858922966,1581698.934,72587.37867,174094.0074,630244.5727,246681.3861,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +8/1/2018,1.1271301780821588,-1.0435180590117648,1585774.425,72802.95909,174605.0764,632227.8545,247408.0355,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +9/1/2018,1.127130178082167,0.10704951911245497,1587635.785,72916.80461,174872.1698,633327.3327,247788.9744,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +10/1/2018,1.1271301780821723,-0.26174424386851475,1583642.662,73063.68049,175218.501,634713.271,248282.1815,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +11/1/2018,1.1271301780821605,-1.3131482481904397,1581707.152,73304.78546,175790.8212,636917.5561,249095.6067,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +12/1/2018,1.1271301780821648,-1.4748888301458958,1580106.614,73561.21148,176399.8862,639254.8926,249961.0977,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +1/1/2019,1.1271301780821645,-1.2858407379824541,1591398.736,73719.92867,177051.8167,641168.9317,250771.7454,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +2/1/2019,1.1271301780821608,0.09821032604562419,1600006.597,73754.81771,177405.9669,642005.2346,251160.7846,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +3/1/2019,1.1271301780821683,1.063585286412749,1606706.505,73703.29761,177551.1225,642087.1138,251254.4201,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +4/1/2019,1.127130178082166,1.3639204607841044,1612276.219,73625.22014,177630.7463,641934.5661,251255.9665,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +5/1/2019,1.1271301780821663,-0.582177748407815,1621655.501,73722.35775,178132.0926,643307.7241,251854.4504,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +6/1/2019,1.127130178082159,-0.5523937240169345,1630977.305,73817.34661,178627.871,644661.3908,252445.2176,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +7/1/2019,1.1271301780821632,0.05107207629410404,1632588.031,73858.49062,178992.7774,645543.6869,252851.268,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +8/1/2019,1.1271301780821679,-0.8434283601786108,1635989.57,73980.708,179553.6871,647133.6517,253534.3951,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +9/1/2019,1.1271301780821639,-0.5604502400503332,1638838.368,74077.96238,180053.7398,648504.7229,254131.7022,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +10/1/2019,1.1271301780821683,0.4945193366917424,1638462.186,74080.36973,180322.5626,649044.1001,254402.9324,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +11/1/2019,1.127130178082165,-0.7621291943280089,1640608.315,74196.77315,180868.2451,650581.0076,255065.0182,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +12/1/2019,1.1271301780821674,0.8834080587034605,1639474.155,74164.77026,181051.4196,650815.1821,255216.1899,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +1/1/2020,1.094009688411116,2.608203639959214,1639370.313,74149.95403,180682.3412,646355.5187,254832.2953,0.0,0.0,0.0,0.0,0.0,0.0,1051.0,1027.0,112.0,110.0,0.0,0.0,NY +2/1/2020,0.624962537191914,1.9997910140230308,1640477.087,74189.92527,180447.7657,642386.453,254637.6909,0.0,0.0,0.0,0.0,0.0,0.0,2099.0,1959.0,1705.0,1587.0,141.0,106.0,NY +3/1/2020,-15.63887727028833,-0.749294226217363,1647091.605,74478.97116,180818.6404,640569.3713,255297.6115,32656.0,32656.0,211.0,211.0,75853.0,2767.0,259565.0,256294.0,56660.0,56096.0,19272.0,17975.0,NY +4/1/2020,-20.289021071857075,-3.284830606262603,1605812.688,75000.58964,181751.6502,640720.0547,256752.2399,104238.0,104238.0,5134.0,5134.0,232456.0,21667.0,445784.0,407230.0,72419.0,71116.0,33508.0,26319.0,NY +5/1/2020,-2.6745741896478954,1.9333856567271746,1554127.814,75046.64576,181530.7996,636789.6961,256577.4454,30573.0,30573.0,2785.0,2785.0,65373.0,6129.0,268897.0,165444.0,12863.0,12534.0,3471.0,2740.0,NY +6/1/2020,-0.3532788937115383,3.9860572785974817,1498793.275,74904.78211,180856.9158,631284.6514,255761.6979,10808.0,10808.0,550.0,550.0,22227.0,1346.0,136776.0,83759.0,5007.0,4906.0,323.0,314.0,NY +7/1/2020,-0.07840818372336755,3.479620917649449,1530233.122,74809.02028,180296.4894,626196.041,255105.5096,11591.0,11591.0,161.0,161.0,22235.0,599.0,117869.0,85984.0,4075.0,4022.0,66.0,66.0,NY +8/1/2020,0.18514705347355703,3.5675227201086344,1561306.753,74705.10093,179718.3701,621064.7363,254423.4711,10653.0,10653.0,111.0,111.0,19010.0,283.0,88262.0,75862.0,3184.0,3144.0,72.0,70.0,NY +9/1/2020,0.14475753169824496,2.5533856842435894,1594146.202,74693.38249,179363.6494,616720.6329,254057.0319,14089.0,14089.0,89.0,89.0,24383.0,367.0,95724.0,93055.0,3320.0,3285.0,144.0,140.0,NY +10/1/2020,-0.46658385054095786,1.3250981911519295,1600708.686,74793.41323,179277.9689,613308.2247,254071.3821,29663.0,29663.0,222.0,222.0,50224.0,401.0,149054.0,140175.0,5385.0,5342.0,179.0,170.0,NY +11/1/2020,-1.648553189199655,1.20019908415716,1607511.746,74904.78301,179219.6222,609990.8059,254124.4052,91730.0,91730.0,739.0,739.0,144652.0,895.0,343145.0,311979.0,9385.0,9229.0,895.0,872.0,NY +12/1/2020,-5.124731521187366,4.614450824769324,1607604.904,74704.41373,178416.8524,604153.6335,253121.2662,212153.0,212153.0,2564.0,2564.0,322370.0,3345.0,610029.0,554816.0,21133.0,20852.0,2512.0,2408.0,NY +1/1/2021,-9.250653381781074,-3.7538153499725215,1609725.891,75656.25657,179051.9365,606527.1992,254708.1931,263125.0,263125.0,3654.0,3654.0,441124.0,5651.0,682529.0,609083.0,35074.0,34683.0,4633.0,4443.0,NY +2/1/2021,-6.551075560476649,-4.081174498580027,1612615.416,76638.10224,179769.6403,609179.0382,256407.7425,107667.0,107667.0,1783.0,1783.0,223960.0,3967.0,401042.0,349294.0,25950.0,25664.0,2539.0,2449.0,NY +3/1/2021,-6.388080750697957,-1.891046734280461,1611320.139,77413.27267,180017.1827,610235.629,257430.4553,107227.0,107227.0,781.0,781.0,229271.0,2680.0,446261.0,384801.0,25398.0,25133.0,2095.0,2028.0,NY +4/1/2021,-3.7114962270638205,-2.3059996094110247,1618945.155,78223.82511,180364.3855,611627.6254,258588.2106,92985.0,92985.0,625.0,625.0,175012.0,1958.0,289569.0,255771.0,16354.0,16161.0,712.0,666.0,NY +5/1/2021,-0.7103028251562091,-2.4289651142695403,1626843.844,79043.03004,180747.2703,613138.4033,259790.3004,32062.0,32062.0,371.0,371.0,54254.0,1043.0,84762.0,76559.0,6209.0,6152.0,55.0,55.0,NY +6/1/2021,0.3353254019848968,-1.9608346389740572,1633841.83,79813.87171,181035.3115,614325.2092,260849.1833,6255.0,6255.0,150.0,150.0,12582.0,372.0,25144.0,22066.0,2676.0,2647.0,0.0,0.0,NY +7/1/2021,-0.3692870563786512,-2.694730057547363,1638460.102,80653.34035,181493.7076,616087.9905,262147.0479,15309.0,15309.0,55.0,55.0,35404.0,171.0,94495.0,80464.0,5059.0,4984.0,48.0,45.0,NY +8/1/2021,-2.4852205220871273,-3.1302692288731673,1644024.223,81534.18524,182057.3266,618206.205,263591.5118,70452.0,70452.0,305.0,305.0,128200.0,613.0,262266.0,238176.0,12209.0,12071.0,302.0,290.0,NY +9/1/2021,-1.6249644798076566,-3.7296590661707176,1650872.201,82474.44524,182763.4879,620807.0081,265237.9332,97576.0,97576.0,678.0,678.0,148093.0,1104.0,277724.0,254648.0,9305.0,9153.0,269.0,256.0,NY +10/1/2021,-0.8316197116193367,-1.5205022491401725,1661674.994,83190.89653,182985.8244,621762.5845,266176.7209,97641.0,97641.0,792.0,792.0,132900.0,1081.0,241388.0,225684.0,6621.0,6534.0,219.0,205.0,NY +11/1/2021,-1.418751415622475,-1.130500142201564,1671647.659,83863.71968,183128.1373,622443.8945,266991.857,131053.0,131053.0,783.0,783.0,170012.0,1095.0,364561.0,343130.0,8608.0,8465.0,490.0,479.0,NY +12/1/2021,-9.984708977847053,-1.6443438355563726,1682631.331,84585.28622,183392.0953,623536.4058,267977.3815,355100.0,355100.0,1390.0,1390.0,750685.0,1974.0,2002692.0,1865917.0,37574.0,36912.0,2619.0,2572.0,NY +1/1/2022,-12.306497302554904,0.4167982700266464,1682520.04,84532.31243,183756.9509,625026.6736,268289.2633,629777.0,629777.0,2316.0,2316.0,1325136.0,5338.0,2146111.0,2039264.0,45406.0,44844.0,5303.0,4988.0,NY +2/1/2022,-0.23692626431553432,0.9804469110053902,1681275.968,84422.95318,183993.8952,626079.0938,268416.8483,76202.0,76202.0,1030.0,1030.0,119805.0,2512.0,190529.0,184384.0,4610.0,4561.0,117.0,117.0,NY +3/1/2022,0.494141210616757,3.923405853680776,1674036.482,84013.25822,183569.0235,624875.7841,267582.2817,40627.0,40627.0,277.0,277.0,70513.0,609.0,137617.0,128413.0,2140.0,2107.0,0.0,0.0,NY +4/1/2022,-0.0702637820617551,-2.1723793478064852,1670064.685,84230.92004,184509.5684,628317.635,268740.4885,108578.0,108578.0,251.0,251.0,172388.0,377.0,324014.0,301879.0,4047.0,3999.0,87.0,87.0,NY +5/1/2022,-1.0741564757160056,1.1827521547478304,1659365.222,84105.92123,184695.7959,629188.8662,268801.7171,151342.0,151342.0,433.0,433.0,269689.0,645.0,549008.0,504975.0,7441.0,7341.0,173.0,171.0,NY +6/1/2022,-1.222576407350947,4.344289313258767,1642453.041,83659.442,184168.7852,627626.6274,267828.2272,71061.0,71061.0,321.0,321.0,173282.0,678.0,335813.0,299986.0,7940.0,7870.0,112.0,109.0,NY +7/1/2022,-2.250135265125272,-4.474910267539592,1652247.84,84116.59792,185626.9939,632827.716,269743.5918,86951.0,86951.0,265.0,265.0,206851.0,602.0,430568.0,379685.0,11418.0,11243.0,271.0,256.0,NY +8/1/2022,-1.149630862664142,-1.4824245061543038,1656069.712,84269.74223,186413.5308,635738.5825,270683.273,73805.0,73805.0,321.0,321.0,170440.0,855.0,284309.0,254339.0,7696.0,7595.0,52.0,48.0,NY +9/1/2022,0.04316419829990237,-0.8295339885771285,1658601.092,84357.47137,187052.6322,638145.247,271410.1036,71875.0,71875.0,297.0,297.0,132929.0,704.0,136978.0,124331.0,3664.0,3616.0,25.0,25.0,NY +10/1/2022,-0.3093911451060517,-0.5255808579922923,1656528.249,84415.46371,187622.6031,640314.4417,272038.0668,16583.0,16583.0,85.0,85.0,0.0,0.0,136088.0,122233.0,4856.0,4791.0,170.0,164.0,NY +11/1/2022,-0.6668294684611846,-2.791349438959642,1659086.449,84708.6767,188713.2915,644259.6705,273421.9682,0.0,0.0,0.0,0.0,0.0,,149083.0,130880.0,6065.0,5974.0,257.0,239.0,NY +12/1/2022,-1.1122574301294392,-3.1297146758068535,1662385.245,85039.4603,189887.0274,648488.0898,274926.4877,0.0,0.0,0.0,0.0,0.0,,201644.0,176701.0,7571.0,7456.0,418.0,390.0,NY diff --git a/reports/figures/results/NY/model.csv b/reports/figures/results/NY/model.csv new file mode 100644 index 0000000..e36e891 --- /dev/null +++ b/reports/figures/results/NY/model.csv @@ -0,0 +1,29 @@ + Model Specification: Dynamic Factor Model +Model: ,Dynamic Factor Model , # of observed variables: ,17 + ,+ 2 factors in 2 blocks, # of factor blocks: ,2 + ,+ AR(1) idiosyncratic , Idiosyncratic disturbances:,AR(1) +Sample:,2012-02-01 00:00:00 , Standardize variables: ,True + ,- 2022-12-01 00:00:00 , , +Observed variables / factor loadings +Dep. variable,Pandemic,Consumption +GDP , ,X +Cons3 , ,X +Cons4 , ,X +Cons5 , ,X +Cons2 , ,X +Cases5 ,X , +Cases2 ,X , +Deaths5 ,X , +Deaths2 ,X , +Cases3 ,X , +Deaths3 ,X , +Cases4 ,X , +Cases1 ,X , +Hosp2 ,X , +Hosp1 ,X , +Deaths4 ,X , +Deaths1 ,X , + Factor blocks: + block ,order +Pandemic ,1 +Consumption,1 diff --git a/reports/figures/results/NY/raw.csv b/reports/figures/results/NY/raw.csv new file mode 100644 index 0000000..2906c34 --- /dev/null +++ b/reports/figures/results/NY/raw.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,1287293.787,65166.80898,155782.9457,531310.2952,220949.671,0.0,0.0,0.0,0.0,0.0,,,,,,, +3/1/2012,1295447.536,65202.73725,155878.3276,531619.3413,221080.9896,0.0,0.0,0.0,0.0,0.0,,,,,,, +4/1/2012,1302797.582,65277.07128,156065.4944,532241.4702,221342.4989,0.0,0.0,0.0,0.0,0.0,,,,,,, +5/1/2012,1313319.833,65510.4056,156632.8,534160.017,222143.1471,0.0,0.0,0.0,0.0,0.0,,,,,,, +6/1/2012,1323042.506,65703.38201,157103.6267,535749.5186,222806.9586,0.0,0.0,0.0,0.0,0.0,,,,,,, +7/1/2012,1329337.979,65839.73331,157439.0582,536877.2976,223278.7497,0.0,0.0,0.0,0.0,0.0,,,,,,, +8/1/2012,1331638.371,65778.86585,157302.8562,536396.8357,223081.6887,0.0,0.0,0.0,0.0,0.0,,,,,,, +9/1/2012,1334335.369,65738.70033,157216.101,536085.0875,222954.7764,0.0,0.0,0.0,0.0,0.0,,,,,,, +10/1/2012,1338003.582,65702.69277,157139.2341,535807.1522,222841.9103,0.0,0.0,0.0,0.0,0.0,,,,,,, +11/1/2012,1346530.86,65905.78378,157634.1911,537479.036,223539.9666,0.0,0.0,0.0,0.0,0.0,,,,,,, +12/1/2012,1354369.191,66074.9754,158048.0749,538874.4744,224123.0503,0.0,0.0,0.0,0.0,0.0,,,,,,, +1/1/2013,1343561.041,66166.43681,158161.5354,539491.9566,224327.9639,0.0,0.0,0.0,0.0,0.0,,,,,,, +2/1/2013,1330488.684,66142.92116,158000.7447,539172.6805,224143.6494,0.0,0.0,0.0,0.0,0.0,,,,,,, +3/1/2013,1324058.284,66448.13557,158625.4615,541533.3884,225073.5724,0.0,0.0,0.0,0.0,0.0,,,,,,, +4/1/2013,1330764.351,66718.44455,159166.6336,543609.361,225885.0452,0.0,0.0,0.0,0.0,0.0,,,,,,, +5/1/2013,1335560.968,66893.01389,159479.3915,544905.2487,226372.3641,0.0,0.0,0.0,0.0,0.0,,,,,,, +6/1/2013,1338338.701,66966.64954,159551.8038,545379.2923,226518.404,0.0,0.0,0.0,0.0,0.0,,,,,,, +7/1/2013,1342243.687,67100.82154,159768.7972,546346.773,226869.5611,0.0,0.0,0.0,0.0,0.0,,,,,,, +8/1/2013,1345741.142,67214.79,159937.97,547150.1002,227152.6943,0.0,0.0,0.0,0.0,0.0,,,,,,, +9/1/2013,1350769.079,67405.31758,160289.5126,548576.8833,227694.7563,0.0,0.0,0.0,0.0,0.0,,,,,,, +10/1/2013,1357112.227,67507.97926,160432.3221,549288.8297,227940.2194,0.0,0.0,0.0,0.0,0.0,,,,,,, +11/1/2013,1363607.935,67618.92142,160595.1388,550068.5522,228213.9702,0.0,0.0,0.0,0.0,0.0,,,,,,, +12/1/2013,1369492.869,67700.32522,160688.1588,550608.4205,228388.386,0.0,0.0,0.0,0.0,0.0,,,,,,, +1/1/2014,1367096.476,67803.91026,161040.2938,551899.4717,228844.1144,0.0,0.0,0.0,0.0,0.0,,,,,,, +2/1/2014,1366496.088,67996.01973,161602.4188,553909.9683,229598.3571,0.0,0.0,0.0,0.0,0.0,,,,,,, +3/1/2014,1363964.971,68091.48357,161934.5787,555132.0178,230025.9891,0.0,0.0,0.0,0.0,0.0,,,,,,, +4/1/2014,1367891.06,68190.54849,162274.8892,556381.681,230465.3728,0.0,0.0,0.0,0.0,0.0,,,,,,, +5/1/2014,1372095.208,68303.82793,162648.6444,557745.711,230952.4157,0.0,0.0,0.0,0.0,0.0,,,,,,, +6/1/2014,1377099.723,68457.22327,163117.6299,559436.0654,231574.8047,0.0,0.0,0.0,0.0,0.0,,,,,,, +7/1/2014,1383134.025,68584.3428,163523.7345,560910.5453,232108.037,0.0,0.0,0.0,0.0,0.0,,,,,,, +8/1/2014,1391412.136,68822.96119,164195.541,563296.3096,233018.4699,0.0,0.0,0.0,0.0,0.0,,,,,,, +9/1/2014,1399163.396,69035.5416,164805.214,565468.9059,233840.7314,0.0,0.0,0.0,0.0,0.0,,,,,,, +10/1/2014,1405392.901,69282.91512,165497.9469,567926.492,234780.8459,0.0,0.0,0.0,0.0,0.0,,,,,,, +11/1/2014,1412351.226,69566.15712,166276.4635,570678.5372,235842.6125,0.0,0.0,0.0,0.0,0.0,,,,,,, +12/1/2014,1420409.462,69903.4389,167184.3793,573874.8786,237087.8181,0.0,0.0,0.0,0.0,0.0,,,,,,, +1/1/2015,1429268.205,70333.62612,168129.8411,578209.0714,238463.4591,0.0,0.0,0.0,0.0,0.0,,,,,,, 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Variable: ,"GDP", and 16 more , No. Observations: ,131 +Model: ,Dynamic Factor Model , Log Likelihood ,-991.522 + ,+ 2 factors in 2 blocks, AIC ,2093.044 + ,+ AR(1) idiosyncratic , BIC ,2251.180 +Date: ,Sat, 26 Oct 2024 , HQIC ,2157.302 +Time: ,18:48:41 , EM Iterations ,358 +Sample: ,02-01-2012 , , + ,- 12-01-2022 , , +Covariance Type:,Not computed , , + Observation equation: +Factor loadings:,Pandemic,Consumption, idiosyncratic: AR(1),var. +GDP ,. ,-0.11 ,0.53 ,0.70 +Cons3 ,. ,-0.44 ,0.91 ,0.06 +Cons4 ,. ,-0.52 ,0.90 ,0.03 +Cons5 ,. ,-0.47 ,0.94 ,0.05 +Cons2 ,. ,-0.55 ,0.93 ,0.00 +Cases5 ,-0.29 ,. ,0.56 ,0.40 +Cases2 ,-0.29 ,. ,0.56 ,0.40 +Deaths5 ,-0.30 ,. ,0.13 ,0.35 +Deaths2 ,-0.30 ,. ,0.13 ,0.35 +Cases3 ,-0.29 ,. ,0.53 ,0.41 +Deaths3 ,-0.28 ,. ,-0.04 ,0.32 +Cases4 ,-0.29 ,. ,0.58 ,0.36 +Cases1 ,-0.29 ,. ,0.60 ,0.35 +Hosp2 ,-0.32 ,. ,-0.12 ,0.00 +Hosp1 ,-0.32 ,. ,-0.13 ,0.00 +Deaths4 ,-0.24 ,. ,0.52 ,0.22 +Deaths1 ,-0.25 ,. ,0.58 ,0.17 + Transition: Factor block 0 + ,L1.Pandemic, error variance +Pandemic,0.65 ,5.67 + Transition: Factor block 1 + ,L1.Consumption, error variance +Consumption,0.47 ,2.62 + +Warnings: +[1] Covariance matrix not calculated. diff --git a/reports/figures/results/NY/run-info.yaml b/reports/figures/results/NY/run-info.yaml new file mode 100644 index 0000000..7a81bae --- /dev/null +++ b/reports/figures/results/NY/run-info.yaml @@ -0,0 +1,45 @@ +diff_cols: [] +factor_map: + Cases1: !!python/tuple + - Pandemic + Cases2: !!python/tuple + - Pandemic + Cases3: !!python/tuple + - Pandemic + Cases4: !!python/tuple + - Pandemic + Cases5: !!python/tuple + - Pandemic + Cons2: !!python/tuple + - Consumption + Cons3: !!python/tuple + - Consumption + Cons4: !!python/tuple + - Consumption + Cons5: !!python/tuple + - Consumption + Deaths1: !!python/tuple + - Pandemic + Deaths2: !!python/tuple + - Pandemic + Deaths3: !!python/tuple + - Pandemic + Deaths4: !!python/tuple + - Pandemic + Deaths5: !!python/tuple + - Pandemic + GDP: !!python/tuple + - Consumption + Hosp1: !!python/tuple + - Pandemic + Hosp2: !!python/tuple + - Pandemic +global_multiplier: 0 +logdiff_cols: +- GDP +- Cons3 +- Cons4 +- Cons5 +- Cons2 +maxiter: 10000 +non_stationary_cols: null diff --git a/reports/figures/results/PA/df.csv b/reports/figures/results/PA/df.csv new file mode 100644 index 0000000..1b0b358 --- /dev/null +++ b/reports/figures/results/PA/df.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,0.5909168090458105,0.2808054412115694,0.18337099420492378,0.5032242999539724,0.1557268123724317,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +3/1/2012,0.5955202069074886,0.2986370370954304,0.20597886676576782,0.521973006747605,0.18329358528027984,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +4/1/2012,0.616504858613189,0.33138919087355234,0.2472904375837003,0.556365092632867,0.23373064027912496,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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+6/1/2017,-1.1881768670339636,-0.09410846439474235,699795.6811,51639.03983,105963.663,351748.2871,157602.6556,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +7/1/2017,-1.1881768670619435,-0.5415548317888204,703160.812,51734.46426,106198.083,352751.4204,157932.5079,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +8/1/2017,-1.1881768670826343,0.8425607504161445,705032.5029,51720.40882,106207.6669,353007.1415,157928.0443,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +9/1/2017,-1.1881768670979336,1.7459060470990273,705919.5613,51635.04428,106070.5834,352774.0138,157705.6042,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +10/1/2017,-1.1881768671092476,0.27462854656636115,706748.1809,51666.80732,106173.9086,353339.2875,157840.7002,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +11/1/2017,-1.1881768671176132,0.40262714435125013,707441.8909,51688.79788,106257.0325,353836.6409,157945.8226,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +12/1/2017,-1.1881768671238,0.27430398710340803,708278.9306,51721.34979,106361.7499,354405.2159,158083.0996,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +1/1/2018,-1.1881768671283743,0.6275976600371296,709968.1762,51807.96433,106327.8286,354647.3225,158135.7929,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +2/1/2018,-1.1881768671317574,-0.08670267114636754,712432.3035,51951.3434,106411.6763,355280.1084,158363.0197,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +3/1/2018,-1.1881768671342583,-0.7474067580620323,715620.6797,52147.67529,106604.7951,356276.3623,158752.4704,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +4/1/2018,-1.1881768671361086,-0.37955914917893585,716316.4734,52314.85597,106739.0204,357074.6132,159053.8764,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +5/1/2018,-1.1881768671374762,-0.40510093104764366,717049.3162,52484.29504,106878.649,357889.597,159362.944,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +6/1/2018,-1.188176867138488,-0.8812444513246148,718316.1461,52692.46748,107097.7778,358969.737,159790.2453,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +7/1/2018,-1.188176867139235,-1.0134226262469879,719885.7498,52911.87421,107340.1619,360127.1095,160252.0361,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +8/1/2018,-1.1881768671397888,-1.0913474521417816,721552.1987,53138.22172,107596.9721,361332.295,160735.1938,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +9/1/2018,-1.1881768671401982,-0.1745744664471024,722211.7383,53290.11017,107703.6087,362032.1553,160993.7188,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +10/1/2018,-1.1881768671405006,-0.46428905248467467,721747.3345,53465.87223,107859.2719,362895.4898,161325.1441,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +11/1/2018,-1.1881768671407242,-1.2960443387875042,722217.8173,53710.44364,108154.1692,364226.5614,161864.6129,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +12/1/2018,-1.1881768671408894,-1.42150353836752,722840.418,53966.19904,108471.7087,365633.6762,162437.9077,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +1/1/2019,-1.1881768671410113,-0.421222563827893,723609.4772,54082.78811,108680.7079,366554.6476,162763.5036,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +2/1/2019,-1.188176867141102,0.6760370104368725,723164.2021,54108.53352,108707.1904,366859.7261,162815.7392,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +3/1/2019,-1.1881768671411688,1.4408179748079062,721875.04,54070.88621,108606.4207,366734.4417,162677.3298,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +4/1/2019,-1.1881768671412185,1.6774572858082588,722594.9171,54013.75478,108466.6606,366476.2555,162480.4457,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +5/1/2019,-1.1881768671412543,0.1304617246424813,725028.856,54085.16604,108585.1251,367089.7281,162670.329,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +6/1/2019,-1.1881768671412822,0.15224754825725695,727439.5779,54155.00068,108700.4594,367692.3182,162855.5055,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +7/1/2019,-1.1881768671413018,0.6294212586603933,729030.8585,54185.33253,108736.5569,368026.4214,162921.9425,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +8/1/2019,-1.1881768671413169,-0.08275389503992014,731420.2654,54275.14256,108892.0568,368764.2708,163167.2599,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA +9/1/2019,-1.1881768671413275,0.13993918726105037,733561.6612,54346.6386,109010.8387,369377.5577,163357.5453,0.0,0.0,0.0,0.0,0.0,,,,,,,,PA 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+8/1/2022,2.304951928068097,-2.449815118399622,741264.1709,64358.45834,120046.9578,362282.0488,184405.4701,95753.0,75145.0,511.0,584.0,105309.0,552.0,152316.0,116802.0,2919.0,2753.0,48.0,41.0,PA +9/1/2022,0.4485767275239769,-1.9150554685834766,742782.9148,64441.19803,120776.3946,363667.26,185217.6531,68601.0,53211.0,497.0,347.0,68601.0,497.0,70589.0,53420.0,1698.0,1600.0,19.0,19.0,PA +10/1/2022,0.04004542061809341,-1.6577200548219542,742953.6816,64501.10868,121458.9201,364917.0431,185960.0956,12431.0,9877.0,110.0,89.0,0.0,0.0,55166.0,41252.0,1669.0,1590.0,60.0,48.0,PA +11/1/2022,-0.15169749586964004,-3.441013367169263,745196.2686,64740.67557,122477.053,367178.9291,187217.8021,0.0,0.0,0.0,0.0,0.0,,48504.0,36869.0,1456.0,1373.0,41.0,35.0,PA +12/1/2022,0.14450426844819364,-3.694296448868446,747769.8724,65008.93365,123548.6223,369602.1921,188557.6361,0.0,0.0,0.0,0.0,0.0,,62529.0,49042.0,1887.0,1808.0,90.0,78.0,PA diff --git a/reports/figures/results/PA/model.csv b/reports/figures/results/PA/model.csv new file mode 100644 index 0000000..e36e891 --- /dev/null +++ b/reports/figures/results/PA/model.csv @@ -0,0 +1,29 @@ + Model Specification: Dynamic Factor Model +Model: ,Dynamic Factor Model , # of observed variables: ,17 + ,+ 2 factors in 2 blocks, # of factor blocks: ,2 + ,+ AR(1) idiosyncratic , Idiosyncratic disturbances:,AR(1) +Sample:,2012-02-01 00:00:00 , Standardize variables: ,True + ,- 2022-12-01 00:00:00 , , +Observed variables / factor loadings +Dep. variable,Pandemic,Consumption +GDP , ,X +Cons3 , ,X +Cons4 , ,X +Cons5 , ,X +Cons2 , ,X +Cases5 ,X , +Cases2 ,X , +Deaths5 ,X , +Deaths2 ,X , +Cases3 ,X , +Deaths3 ,X , +Cases4 ,X , +Cases1 ,X , +Hosp2 ,X , +Hosp1 ,X , +Deaths4 ,X , +Deaths1 ,X , + Factor blocks: + block ,order +Pandemic ,1 +Consumption,1 diff --git a/reports/figures/results/PA/raw.csv b/reports/figures/results/PA/raw.csv new file mode 100644 index 0000000..74f12ac --- /dev/null +++ b/reports/figures/results/PA/raw.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,643047.2985,49689.1858,105728.8831,318906.5582,155418.1359,0.0,0.0,0.0,0.0,0.0,,,,,,, +3/1/2012,643289.0159,49704.21617,105645.6861,318934.3761,155349.9524,0.0,0.0,0.0,0.0,0.0,,,,,,, +4/1/2012,644471.859,49748.56307,105625.1711,319150.5284,155373.7675,0.0,0.0,0.0,0.0,0.0,,,,,,, +5/1/2012,647224.4054,49914.08866,105861.9621,320144.0878,155776.0675,0.0,0.0,0.0,0.0,0.0,,,,,,, +6/1/2012,649578.9537,50048.84458,106033.3086,320940.1829,156082.1532,0.0,0.0,0.0,0.0,0.0,,,,,,, +7/1/2012,651012.3989,50140.46521,106113.2528,321459.6618,156253.7013,0.0,0.0,0.0,0.0,0.0,,,,,,, +8/1/2012,650495.4617,50081.93855,105875.8598,321016.7708,155957.765,0.0,0.0,0.0,0.0,0.0,,,,,,, +9/1/2012,650182.7075,50039.25124,105672.6756,320675.8386,155711.877,0.0,0.0,0.0,0.0,0.0,,,,,,, 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Variable: ,"GDP", and 16 more , No. Observations: ,131 +Model: ,Dynamic Factor Model , Log Likelihood ,-726.875 + ,+ 2 factors in 2 blocks, AIC ,1563.750 + ,+ AR(1) idiosyncratic , BIC ,1721.886 +Date: ,Sat, 26 Oct 2024 , HQIC ,1628.007 +Time: ,18:48:43 , EM Iterations ,318 +Sample: ,02-01-2012 , , + ,- 12-01-2022 , , +Covariance Type:,Not computed , , + Observation equation: +Factor loadings:,Pandemic,Consumption, idiosyncratic: AR(1),var. +GDP ,. ,-0.06 ,0.53 ,0.72 +Cons3 ,. ,-0.47 ,0.94 ,0.04 +Cons4 ,. ,-0.55 ,0.94 ,0.02 +Cons5 ,. ,-0.38 ,0.91 ,0.12 +Cons2 ,. ,-0.57 ,0.97 ,0.00 +Cases5 ,0.29 ,. ,0.02 ,0.02 +Cases2 ,0.29 ,. ,-0.01 ,0.02 +Deaths5 ,0.29 ,. ,0.36 ,0.37 +Deaths2 ,0.28 ,. ,0.38 ,0.39 +Cases3 ,0.29 ,. ,-0.05 ,0.02 +Deaths3 ,0.29 ,. ,0.36 ,0.36 +Cases4 ,0.29 ,. ,0.89 ,0.00 +Cases1 ,0.29 ,. ,0.52 ,0.00 +Hosp2 ,0.30 ,. ,0.53 ,0.19 +Hosp1 ,0.30 ,. ,0.54 ,0.20 +Deaths4 ,0.27 ,. ,0.48 ,0.44 +Deaths1 ,0.26 ,. ,0.47 ,0.46 + Transition: Factor block 0 + ,L1.Pandemic, error variance +Pandemic,0.71 ,5.73 + Transition: Factor block 1 + ,L1.Consumption, error variance +Consumption,0.61 ,1.86 + +Warnings: +[1] Covariance matrix not calculated. diff --git a/reports/figures/results/PA/run-info.yaml b/reports/figures/results/PA/run-info.yaml new file mode 100644 index 0000000..7a81bae --- /dev/null +++ b/reports/figures/results/PA/run-info.yaml @@ -0,0 +1,45 @@ +diff_cols: [] +factor_map: + Cases1: !!python/tuple + - Pandemic + Cases2: !!python/tuple + - Pandemic + Cases3: !!python/tuple + - Pandemic + Cases4: !!python/tuple + - Pandemic + Cases5: !!python/tuple + - Pandemic + Cons2: !!python/tuple + - Consumption + Cons3: !!python/tuple + - Consumption + Cons4: !!python/tuple + - Consumption + Cons5: !!python/tuple + - Consumption + Deaths1: !!python/tuple + - Pandemic + Deaths2: !!python/tuple + - Pandemic + Deaths3: !!python/tuple + - Pandemic + Deaths4: !!python/tuple + - Pandemic + Deaths5: !!python/tuple + - Pandemic + GDP: !!python/tuple + - Consumption + Hosp1: !!python/tuple + - Pandemic + Hosp2: !!python/tuple + - Pandemic +global_multiplier: 0 +logdiff_cols: +- GDP +- Cons3 +- Cons4 +- Cons5 +- Cons2 +maxiter: 10000 +non_stationary_cols: null diff --git a/reports/figures/results/RI/df.csv b/reports/figures/results/RI/df.csv new file mode 100644 index 0000000..d1d5e34 --- /dev/null +++ b/reports/figures/results/RI/df.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,0.5716971470244915,0.2737808656394498,0.3200598115202634,0.3352212514622723,0.3056409199257348,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +3/1/2012,0.5763220156112546,0.2909768287342773,0.34622076516084277,0.35740017414381486,0.3284283615574716,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +4/1/2012,0.5671333710896798,0.32244580262326494,0.3940423779803149,0.3978869702846107,0.3700968192204258,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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+2/1/2012,1.0304318914279422,1.8262081799525574,51334.00305,3364.893599,7352.269493,27177.876,10717.16309,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +3/1/2012,1.015946715975998,1.6362832174530593,51413.84669,3363.58851,7347.295333,27145.86153,10710.88384,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +4/1/2012,1.0103452937555102,1.2315325925539011,51461.72538,3364.274795,7346.677882,27129.98752,10710.95268,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +5/1/2012,1.0081826360890822,-0.6009648484231538,51634.95532,3373.156298,7363.957001,27180.20497,10737.1133,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +6/1/2012,1.0073525189808163,-0.07934353192420529,51776.35802,3379.954666,7376.684988,27213.60226,10756.63965,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +7/1/2012,1.0070348322368219,0.623165301160149,51806.56373,3383.83965,7383.054274,27223.53954,10766.89392,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +8/1/2012,1.006913404291679,2.9662298732431376,51681.87193,3377.600133,7367.341161,27152.10259,10744.94129,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI 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+1/1/2015,1.006838345153704,-3.8251493648063093,54312.10657,3617.137559,7873.810569,28485.40965,11491.03759,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +2/1/2015,1.0068383451537035,1.0302571634325108,54295.45765,3625.699086,7873.511702,28508.24288,11499.29198,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +3/1/2015,1.0068383451537035,1.2429717338867088,54264.08379,3633.242894,7871.070008,28523.23229,11504.38584,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +4/1/2015,1.0068383451537037,0.3583613548547334,54410.35857,3644.932465,7877.660091,28570.88718,11522.65734,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +5/1/2015,1.0068383451537037,1.6149034127588138,54468.08717,3650.6844,7871.476211,28572.13831,11522.21716,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +6/1/2015,1.0068383451537037,1.2705521975992953,54549.80843,3658.036302,7868.822323,28586.10047,11526.907,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +7/1/2015,1.0068383451537037,0.41286234568695657,54509.68829,3669.43475,7874.92387,28631.8046,11544.3989,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +8/1/2015,1.0068383451537037,-0.2107967126776568,54513.86606,3683.803484,7887.41517,28700.72118,11571.25088,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +9/1/2015,1.0068383451537035,-1.3580633751528473,54599.3281,3703.685775,7911.684691,28812.53086,11615.39467,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +10/1/2015,1.0068383451537037,-0.3213175133262296,54798.40037,3718.651355,7925.421694,28886.02165,11644.08919,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +11/1/2015,1.0068383451537037,0.26679020251793384,54956.01769,3730.803101,7933.170113,28937.67479,11663.98128,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +12/1/2015,1.006838345153704,-0.8291931116204991,55191.90726,3748.268815,7952.214464,29030.53664,11700.48328,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +1/1/2016,1.0068383451537037,0.13900862189994623,54962.24808,3751.823063,7970.511555,29072.1336,11722.33462,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +2/1/2016,1.0068383451537035,-0.7327773372685757,54794.85093,3759.629479,7997.843255,29146.68126,11757.47273,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +3/1/2016,1.0068383451537037,1.5382068620218896,54467.29351,3756.414221,8001.710459,29135.75922,11758.12468,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +4/1/2016,1.0068383451537037,2.304945190390479,54539.00709,3749.490318,7997.61774,29095.9935,11747.10806,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +5/1/2016,1.0068383451537035,0.9034762466828687,54708.99013,3749.378869,8008.004975,29109.02571,11757.38384,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +6/1/2016,1.0068383451537035,1.2323276978173694,54855.29158,3747.684185,8014.974802,29109.71922,11762.65899,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +7/1/2016,1.0068383451537037,0.08447514355793251,55124.89845,3751.567228,8033.848919,29153.70505,11785.41615,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +8/1/2016,1.0068383451537035,0.9260114583486683,55334.31059,3751.381251,8043.989244,29166.04391,11795.37049,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +9/1/2016,1.0068383451537037,1.1131454397789267,55529.81684,3750.298886,8052.173497,29171.36914,11802.47238,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +10/1/2016,1.0068383451537035,1.4838712990381075,55339.54918,3747.431273,8056.483372,29162.75393,11803.91465,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +11/1/2016,1.0068383451537035,0.07668424795367645,55250.60577,3751.399144,8075.461559,29207.29749,11826.8607,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +12/1/2016,1.0068383451537037,1.193281901198384,55082.41523,3749.964291,8082.786596,29209.74699,11832.75089,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +1/1/2017,1.0068383451537037,1.7056223928803425,54689.30252,3747.462242,8083.376025,29172.24815,11830.83827,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +2/1/2017,1.006838345153704,0.029228954211194758,54416.8798,3753.102207,8101.496637,29198.22551,11854.59884,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +3/1/2017,1.006838345153704,-1.573030399077442,54258.18864,3766.562097,8136.491584,29285.05762,11903.05368,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +4/1/2017,1.0068383451537037,0.5598974387847262,54289.3529,3769.670147,8149.114843,29291.4334,11918.78499,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI 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+12/1/2017,1.0068383451537037,0.057197730124991186,54799.21481,3816.040381,8295.943167,29511.49748,12111.98355,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +1/1/2018,1.0068383451537037,0.7911260518307728,54901.14441,3815.068025,8309.271437,29504.55003,12124.33946,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +2/1/2018,1.006838345153704,-0.05710606645578942,55063.16686,3818.319783,8331.707317,29530.26713,12150.0271,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +3/1/2018,1.0068383451537037,-0.841510431017019,55281.27523,3825.491524,8362.637669,29586.29856,12188.12919,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +4/1/2018,1.0068383451537035,-0.40440322890309066,55290.98912,3830.549073,8388.895062,29625.97696,12219.44414,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +5/1/2018,1.0068383451537037,-0.43440127839302756,55303.73191,3835.799605,8415.516715,29667.14575,12251.31632,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +6/1/2018,1.0068383451537037,-0.9993984680355192,55357.79241,3843.902619,8448.350674,29730.37473,12292.25329,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI 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+2/1/2019,1.0068383451537035,1.1868359402970345,56281.25019,3901.517234,8676.329565,30229.21897,12577.8468,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +3/1/2019,1.0068383451537037,2.0926982355265125,56443.90231,3897.119962,8672.368265,30219.95468,12569.48823,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +4/1/2019,1.0068383451537037,2.3715927008231334,56379.57734,3891.328111,8665.269934,30199.7328,12556.59804,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +5/1/2019,1.006838345153704,0.5325211940599912,56449.44291,3894.803197,8678.785562,30251.3366,12573.58876,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +6/1/2019,1.0068383451537037,0.5565823521180591,56517.66953,3898.167099,8692.045299,30302.04212,12590.2124,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +7/1/2019,1.0068383451537035,1.1214425815205975,56594.9098,3898.691118,8698.960282,30330.61899,12597.6514,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +8/1/2019,1.0068383451537037,0.27402869390249673,56734.1917,3903.497621,8715.420284,30392.46843,12618.9179,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +9/1/2019,1.0068383451537037,0.5368794689322827,56854.2833,3906.988667,8728.937363,30444.05074,12635.92603,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +10/1/2019,1.006838345153704,1.5275310517010108,57010.61675,3905.481648,8731.272772,30456.62287,12636.75442,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +11/1/2019,1.0068383451537037,0.338716043596651,57254.31267,3909.988323,8747.039139,30516.03466,12657.02746,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +12/1/2019,1.0068383451537035,1.8862989569095328,57383.05519,3906.678948,8745.304147,30514.37654,12651.98309,0.0,0.0,0.0,0.0,0.0,,,,,,,,RI +1/1/2020,1.0068383451537037,1.2504967933541664,57196.75392,3910.035694,8747.513761,30394.86888,12657.54945,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,RI +2/1/2020,1.0068383451537037,0.6807281467004611,57053.2397,3916.26902,8756.174785,30298.16032,12672.4438,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,RI +3/1/2020,0.719640036574299,-1.9072319047297341,57101.07919,3935.654732,8794.236808,30303.04808,12729.89154,543.0,543.0,8.0,8.0,488.0,11.0,,,,,,,RI +4/1/2020,-8.255307106527244,-4.293798495820443,55606.98011,3967.361231,8859.789968,30401.71025,12827.1512,7968.0,7968.0,258.0,258.0,8133.0,353.0,,,,,,,RI +5/1/2020,-15.219866089544997,0.6302686685430547,53752.26168,3973.929118,8869.181928,30307.12048,12843.11105,5755.0,5755.0,452.0,452.0,6307.0,463.0,,,,,,,RI +6/1/2020,-7.286012815046189,2.569263862315056,51771.62155,3970.527152,8856.346956,30137.16071,12826.87411,1781.0,1781.0,231.0,231.0,1885.0,150.0,,,,,,,RI +7/1/2020,-1.039449603472776,2.0953726454987374,53364.12417,3969.542279,8848.937326,29986.48024,12818.4796,2044.0,2044.0,57.0,57.0,2209.0,49.0,,,,,,,RI +8/1/2020,-0.46505298631803016,2.1819577434936326,54940.89453,3968.100098,8840.539384,29833.20019,12808.63948,2724.0,2724.0,41.0,41.0,2927.0,52.0,,,,,,,RI +9/1/2020,-1.3625477006175806,1.2295737645415519,56577.45961,3971.535574,8843.033518,29717.28204,12814.56909,2821.0,2821.0,66.0,66.0,2799.0,47.0,,,,,,,RI +10/1/2020,-2.2241426263604622,0.07544262085354767,56949.19151,3980.904271,8858.749522,29646.06024,12839.65379,8109.0,8109.0,90.0,90.0,8126.0,91.0,5924.0,5912.0,0.0,0.0,0.0,0.0,RI +11/1/2020,-4.952526557879592,-0.038313360146023046,57329.41689,3990.874572,8875.806721,29579.38555,12866.68129,22535.0,22535.0,166.0,166.0,23849.0,199.0,29538.0,27951.0,0.0,0.0,0.0,0.0,RI +12/1/2020,-13.67617518101122,3.1847010563400486,57469.72804,3984.217566,8855.907267,29390.10254,12840.12483,31118.0,31118.0,409.0,409.0,31226.0,515.0,41228.0,39078.0,0.0,0.0,438.0,438.0,RI +1/1/2021,-12.778680466687923,-5.0686555447038595,57137.01595,4035.208098,8916.014555,29566.16452,12951.22265,24700.0,24700.0,384.0,384.0,26489.0,411.0,25598.0,24548.0,0.0,0.0,472.0,460.0,RI +2/1/2021,-11.34268892370116,-5.335585669139245,56834.51221,4087.797263,8980.031456,29755.35804,13067.82872,10023.0,10023.0,344.0,344.0,11184.0,161.0,9422.0,6710.0,0.0,0.0,174.0,152.0,RI +3/1/2021,-2.762639454949833,-3.230538768466995,56388.25331,4129.360567,9020.30238,29866.08086,13149.66295,10372.0,10372.0,105.0,105.0,11707.0,117.0,23551.0,20431.0,0.0,0.0,24.0,24.0,RI +4/1/2021,-1.111249180631965,-3.58347380903922,56879.2054,4172.808814,9065.255578,29992.55939,13238.06439,10229.0,10229.0,59.0,59.0,10857.0,52.0,52318.0,49444.0,0.0,0.0,0.0,0.0,RI +5/1/2021,-0.42915319774471206,-3.6623345253353463,57377.46656,4216.716455,9111.717863,30124.26079,13328.43432,3425.0,3425.0,40.0,40.0,3557.0,37.0,25630.0,24744.0,0.0,0.0,0.0,0.0,RI +6/1/2021,0.32474236226757913,-3.1851718088812153,57841.63938,4258.041885,9153.111954,30239.43772,13411.15384,654.0,654.0,19.0,19.0,870.0,22.0,40652.0,40644.0,0.0,0.0,0.0,0.0,RI +7/1/2021,0.6478404594260392,-3.84245442063005,57808.38492,4303.026515,9202.849194,30382.39529,13505.87571,1864.0,1864.0,10.0,10.0,1726.0,10.0,36450.0,4722.0,0.0,0.0,0.0,0.0,RI +8/1/2021,-0.07015531201944653,-4.219605999154236,57810.1634,4350.216986,9257.697548,30542.40665,13607.91453,7682.0,7682.0,30.0,30.0,8278.0,30.0,196.0,170.0,0.0,0.0,32.0,24.0,RI +9/1/2021,-1.4343472777686026,-4.752324226903216,57858.45148,4400.576159,9319.609958,30725.87066,13720.18612,9526.0,9526.0,68.0,68.0,9744.0,68.0,412.0,356.0,2.0,2.0,0.0,0.0,RI +10/1/2021,-0.46505298631633646,-2.6381628162211195,58357.18157,4438.991933,9356.622602,30827.42448,13795.61454,7333.0,7333.0,41.0,41.0,7044.0,40.0,1996.0,1470.0,0.0,0.0,0.0,0.0,RI +11/1/2021,-0.9317502377616121,-2.2402985801762543,58825.35251,4475.077552,9389.241399,30914.74196,13864.31895,11643.0,11643.0,54.0,54.0,12358.0,54.0,7695.0,4661.0,0.0,0.0,26.0,22.0,RI +12/1/2021,-4.090931632139174,-2.695556288784659,59327.75726,4513.762085,9427.806902,31021.86956,13941.56899,39895.0,39895.0,142.0,142.0,39333.0,134.0,23615.0,12445.0,0.0,0.0,22.0,14.0,RI +1/1/2022,-8.686104569431958,2.0091394000741616,58911.7121,4508.401064,9425.408823,31095.73753,13933.80989,103453.0,103453.0,270.0,270.0,112683.0,250.0,55810.0,32991.0,0.0,0.0,0.0,0.0,RI +2/1/2022,-2.4395413577917178,2.5253156070051643,58460.58902,4500.061021,9416.684695,31147.82517,13916.74572,11682.0,11682.0,96.0,96.0,12146.0,96.0,15857.0,907.0,0.0,0.0,0.0,0.0,RI +3/1/2022,-1.9369443177884116,5.285074805417843,57807.22216,4475.750282,9374.407563,31087.69298,13850.15785,5402.0,5402.0,82.0,82.0,5337.0,110.0,25590.0,1104.0,0.0,0.0,0.0,0.0,RI +4/1/2022,0.36064215083785833,-0.4774066269276638,58401.67924,4484.889865,9402.09329,31258.66149,13886.98316,10432.0,10432.0,18.0,18.0,10367.0,18.0,34912.0,740.0,0.0,0.0,0.0,0.0,RI +5/1/2022,-0.3932534091856336,2.6717946014314213,58755.00136,4475.804034,9391.503134,31301.74434,13867.30717,19979.0,19979.0,39.0,39.0,21161.0,39.0,60668.0,1600.0,0.0,0.0,0.0,0.0,RI +6/1/2022,-0.07015531202834413,5.638438883601378,58876.7893,4449.648617,9344.962433,31223.76773,13794.61105,9409.0,9409.0,30.0,30.0,9979.0,30.0,40992.0,25924.0,0.0,0.0,0.0,0.0,RI +7/1/2022,0.25294278513390583,-2.6904021296160145,59252.81814,4471.576835,9399.330346,31482.26186,13870.90718,7828.0,7828.0,21.0,21.0,7630.0,21.0,706.0,334.0,0.0,0.0,0.0,0.0,RI +8/1/2022,0.5401410937075626,0.118049179384216,59414.603,4477.348162,9419.721755,31626.8212,13897.06992,5419.0,5419.0,13.0,13.0,6239.0,13.0,14336.0,11887.0,0.0,0.0,0.0,0.0,RI +9/1/2022,0.07344384226893624,0.7205520211901664,59529.93862,4479.658328,9432.781062,31746.29895,13912.43939,8489.0,8489.0,26.0,26.0,8823.0,26.0,7888.0,2639.0,0.0,0.0,0.0,0.0,RI +10/1/2022,0.8273394022919411,0.9942244932735073,59417.09529,4480.406239,9442.491889,31853.96489,13922.89813,1709.0,1709.0,5.0,5.0,0.0,0.0,7220.0,2719.0,0.0,0.0,0.0,0.0,RI +11/1/2022,1.0068383451546081,-1.1547270267077216,59470.54234,4493.649661,9478.498632,32049.98504,13972.14829,0.0,0.0,0.0,0.0,0.0,,169.0,153.0,0.0,0.0,0.0,0.0,RI +12/1/2022,1.0068383451521004,-1.4860796576134687,59550.59331,4508.889583,9518.704581,32260.09342,14027.59416,0.0,0.0,0.0,0.0,0.0,,6438.0,2675.0,0.0,0.0,11.0,4.0,RI diff --git a/reports/figures/results/RI/model.csv b/reports/figures/results/RI/model.csv new file mode 100644 index 0000000..e36e891 --- /dev/null +++ b/reports/figures/results/RI/model.csv @@ -0,0 +1,29 @@ + Model Specification: Dynamic Factor Model +Model: ,Dynamic Factor Model , # of observed variables: ,17 + ,+ 2 factors in 2 blocks, # of factor blocks: ,2 + ,+ AR(1) idiosyncratic , Idiosyncratic disturbances:,AR(1) +Sample:,2012-02-01 00:00:00 , Standardize variables: ,True + ,- 2022-12-01 00:00:00 , , +Observed variables / factor loadings +Dep. variable,Pandemic,Consumption +GDP , ,X +Cons3 , ,X +Cons4 , ,X +Cons5 , ,X +Cons2 , ,X +Cases5 ,X , +Cases2 ,X , +Deaths5 ,X , +Deaths2 ,X , +Cases3 ,X , +Deaths3 ,X , +Cases4 ,X , +Cases1 ,X , +Hosp2 ,X , +Hosp1 ,X , +Deaths4 ,X , +Deaths1 ,X , + Factor blocks: + block ,order +Pandemic ,1 +Consumption,1 diff --git a/reports/figures/results/RI/raw.csv b/reports/figures/results/RI/raw.csv new file mode 100644 index 0000000..90d7c10 --- /dev/null +++ b/reports/figures/results/RI/raw.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,51334.00305,3364.893599,7352.269493,27177.876,10717.16309,0.0,0.0,0.0,0.0,0.0,,,,,,, +3/1/2012,51413.84669,3363.58851,7347.295333,27145.86153,10710.88384,0.0,0.0,0.0,0.0,0.0,,,,,,, +4/1/2012,51461.72538,3364.274795,7346.677882,27129.98752,10710.95268,0.0,0.0,0.0,0.0,0.0,,,,,,, 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+12/1/2022,59550.59331,4508.889583,9518.704581,32260.09342,14027.59416,0.0,0.0,0.0,0.0,0.0,,6438.0,2675.0,0.0,0.0,11.0,4.0 diff --git a/reports/figures/results/RI/results.csv b/reports/figures/results/RI/results.csv new file mode 100644 index 0000000..eafa1c4 --- /dev/null +++ b/reports/figures/results/RI/results.csv @@ -0,0 +1,38 @@ + Dynamic Factor Results +Dep. Variable: ,"GDP", and 16 more , No. Observations: ,131 +Model: ,Dynamic Factor Model , Log Likelihood ,-122.226 + ,+ 2 factors in 2 blocks, AIC ,354.452 + ,+ AR(1) idiosyncratic , BIC ,512.588 +Date: ,Sat, 26 Oct 2024 , HQIC ,418.710 +Time: ,18:48:43 , EM Iterations ,44 +Sample: ,02-01-2012 , , + ,- 12-01-2022 , , +Covariance Type:,Not computed , , + Observation equation: +Factor loadings:,Pandemic,Consumption, idiosyncratic: AR(1),var. +GDP ,. ,-0.04 ,0.50 ,0.75 +Cons3 ,. ,-0.49 ,0.83 ,0.04 +Cons4 ,. ,-0.51 ,0.82 ,0.02 +Cons5 ,. ,-0.46 ,0.93 ,0.07 +Cons2 ,. ,-0.52 ,0.87 ,0.00 +Cases5 ,-0.33 ,. ,0.51 ,0.60 +Cases2 ,-0.33 ,. ,0.51 ,0.60 +Deaths5 ,-0.35 ,. ,0.38 ,0.00 +Deaths2 ,-0.35 ,. ,0.38 ,0.00 +Cases3 ,-0.33 ,. ,0.48 ,0.62 +Deaths3 ,-0.35 ,. ,0.01 ,0.07 +Cases4 ,-0.28 ,. ,0.65 ,0.55 +Cases1 ,-0.28 ,. ,0.49 ,0.67 +Hosp2 ,-0.02 ,. ,-0.01 ,1.00 +Hosp1 ,-0.02 ,. ,-0.01 ,1.00 +Deaths4 ,-0.27 ,. ,0.51 ,0.44 +Deaths1 ,-0.27 ,. ,0.49 ,0.46 + Transition: Factor block 0 + ,L1.Pandemic, error variance +Pandemic,0.78 ,3.27 + Transition: Factor block 1 + ,L1.Consumption, error variance +Consumption,0.53 ,2.54 + +Warnings: +[1] Covariance matrix not calculated. diff --git a/reports/figures/results/RI/run-info.yaml b/reports/figures/results/RI/run-info.yaml new file mode 100644 index 0000000..7a81bae --- /dev/null +++ b/reports/figures/results/RI/run-info.yaml @@ -0,0 +1,45 @@ +diff_cols: [] +factor_map: + Cases1: !!python/tuple + - Pandemic + Cases2: !!python/tuple + - Pandemic + Cases3: !!python/tuple + - Pandemic + Cases4: !!python/tuple + - Pandemic + Cases5: !!python/tuple + - Pandemic + Cons2: !!python/tuple + - Consumption + Cons3: !!python/tuple + - Consumption + Cons4: !!python/tuple + - Consumption + Cons5: !!python/tuple + - Consumption + Deaths1: !!python/tuple + - Pandemic + Deaths2: !!python/tuple + - Pandemic + Deaths3: !!python/tuple + - Pandemic + Deaths4: !!python/tuple + - Pandemic + Deaths5: !!python/tuple + - Pandemic + GDP: !!python/tuple + - Consumption + Hosp1: !!python/tuple + - Pandemic + Hosp2: !!python/tuple + - Pandemic +global_multiplier: 0 +logdiff_cols: +- GDP +- Cons3 +- Cons4 +- Cons5 +- Cons2 +maxiter: 10000 +non_stationary_cols: null diff --git a/reports/figures/results/VA/df.csv b/reports/figures/results/VA/df.csv new file mode 100644 index 0000000..ae5c4fa --- /dev/null +++ b/reports/figures/results/VA/df.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,0.5306920512712817,0.39228054810473595,0.2555318863462852,0.4948512744342686,0.3141249851246008,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +3/1/2012,0.5380297008722275,0.40897626081892213,0.28427842302229855,0.5143156119679351,0.3383718600996768,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +4/1/2012,0.5880234622283111,0.43991724026922296,0.3370315273431367,0.5501115579432743,0.3829820590908405,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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+11/1/2022,1.117058996093227,-2.2663419180369084,544562.8915,43967.06455,80620.62437,235350.7918,124587.6889,0.0,0.0,0.0,0.0,0.0,,30932.0,18725.0,688.0,645.0,0.0,0.0,VA +12/1/2022,1.114666809400993,-2.566350852940505,547669.479,44097.62117,81142.8663,236944.7716,125240.4875,0.0,0.0,0.0,0.0,0.0,,60181.0,35493.0,1156.0,1079.0,0.0,0.0,VA diff --git a/reports/figures/results/VA/model.csv b/reports/figures/results/VA/model.csv new file mode 100644 index 0000000..e36e891 --- /dev/null +++ b/reports/figures/results/VA/model.csv @@ -0,0 +1,29 @@ + Model Specification: Dynamic Factor Model +Model: ,Dynamic Factor Model , # of observed variables: ,17 + ,+ 2 factors in 2 blocks, # of factor blocks: ,2 + ,+ AR(1) idiosyncratic , Idiosyncratic disturbances:,AR(1) +Sample:,2012-02-01 00:00:00 , Standardize variables: ,True + ,- 2022-12-01 00:00:00 , , +Observed variables / factor loadings +Dep. variable,Pandemic,Consumption +GDP , ,X +Cons3 , ,X +Cons4 , ,X +Cons5 , ,X +Cons2 , ,X +Cases5 ,X , +Cases2 ,X , +Deaths5 ,X , +Deaths2 ,X , +Cases3 ,X , +Deaths3 ,X , +Cases4 ,X , +Cases1 ,X , +Hosp2 ,X , +Hosp1 ,X , +Deaths4 ,X , +Deaths1 ,X , + Factor blocks: + block ,order +Pandemic ,1 +Consumption,1 diff --git a/reports/figures/results/VA/raw.csv b/reports/figures/results/VA/raw.csv new file mode 100644 index 0000000..ada9d0d --- /dev/null +++ b/reports/figures/results/VA/raw.csv @@ -0,0 +1,132 @@ +Time,GDP,Cons3,Cons4,Cons5,Cons2,Cases5,Cases2,Deaths5,Deaths2,Cases3,Deaths3,Cases4,Cases1,Hosp2,Hosp1,Deaths4,Deaths1 +2/1/2012,443238.234,31740.70353,68754.0141,200366.5511,100494.7176,0.0,0.0,0.0,0.0,0.0,,,,,,, +3/1/2012,442745.676,31785.45646,68764.80172,200465.1075,100550.2582,0.0,0.0,0.0,0.0,0.0,,,,,,, +4/1/2012,443234.4675,31848.84412,68816.18035,200681.7803,100665.0245,0.0,0.0,0.0,0.0,0.0,,,,,,, +5/1/2012,444802.8818,31989.80325,69035.18489,201387.271,101024.9881,0.0,0.0,0.0,0.0,0.0,,,,,,, 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+++ b/reports/figures/results/VA/results.csv @@ -0,0 +1,38 @@ + Dynamic Factor Results +Dep. Variable: ,"GDP", and 16 more , No. Observations: ,131 +Model: ,Dynamic Factor Model , Log Likelihood ,-708.236 + ,+ 2 factors in 2 blocks, AIC ,1526.471 + ,+ AR(1) idiosyncratic , BIC ,1684.607 +Date: ,Sat, 26 Oct 2024 , HQIC ,1590.729 +Time: ,18:48:45 , EM Iterations ,408 +Sample: ,02-01-2012 , , + ,- 12-01-2022 , , +Covariance Type:,Not computed , , + Observation equation: +Factor loadings:,Pandemic,Consumption, idiosyncratic: AR(1),var. +GDP ,. ,-0.12 ,0.54 ,0.70 +Cons3 ,. ,-0.49 ,0.92 ,0.04 +Cons4 ,. ,-0.54 ,0.93 ,0.02 +Cons5 ,. ,-0.37 ,0.89 ,0.16 +Cons2 ,. ,-0.56 ,0.97 ,0.00 +Cases5 ,-0.31 ,. ,0.01 ,0.00 +Cases2 ,-0.31 ,. ,0.70 ,0.00 +Deaths5 ,-0.24 ,. ,0.11 ,0.74 +Deaths2 ,-0.24 ,. ,0.15 ,0.74 +Cases3 ,-0.31 ,. ,-0.25 ,0.00 +Deaths3 ,-0.24 ,. ,0.11 ,0.74 +Cases4 ,-0.31 ,. ,0.48 ,0.03 +Cases1 ,-0.31 ,. ,0.59 ,0.02 +Hosp2 ,-0.31 ,. ,0.55 ,0.28 +Hosp1 ,-0.31 ,. ,0.56 ,0.27 +Deaths4 ,-0.28 ,. ,0.60 ,0.28 +Deaths1 ,-0.28 ,. ,0.58 ,0.28 + Transition: Factor block 0 + ,L1.Pandemic, error variance +Pandemic,0.60 ,6.87 + Transition: Factor block 1 + ,L1.Consumption, error variance +Consumption,0.55 ,2.13 + +Warnings: +[1] Covariance matrix not calculated. diff --git a/reports/figures/results/VA/run-info.yaml b/reports/figures/results/VA/run-info.yaml new file mode 100644 index 0000000..7a81bae --- /dev/null +++ b/reports/figures/results/VA/run-info.yaml @@ -0,0 +1,45 @@ +diff_cols: [] +factor_map: + Cases1: !!python/tuple + - Pandemic + Cases2: !!python/tuple + - Pandemic + Cases3: !!python/tuple + - Pandemic + Cases4: !!python/tuple + - Pandemic + Cases5: !!python/tuple + - Pandemic + Cons2: !!python/tuple + - Consumption + Cons3: !!python/tuple + - Consumption + Cons4: !!python/tuple + - Consumption + Cons5: !!python/tuple + - Consumption + Deaths1: !!python/tuple + - Pandemic + Deaths2: !!python/tuple + - Pandemic + Deaths3: !!python/tuple + - Pandemic + Deaths4: !!python/tuple + - Pandemic + Deaths5: !!python/tuple + - Pandemic + GDP: !!python/tuple + - Consumption + Hosp1: !!python/tuple + - Pandemic + Hosp2: !!python/tuple + - Pandemic +global_multiplier: 0 +logdiff_cols: +- GDP +- Cons3 +- Cons4 +- Cons5 +- Cons2 +maxiter: 10000 +non_stationary_cols: null diff --git a/reports/figures/results/factors.csv b/reports/figures/results/factors.csv new file mode 100644 index 0000000..e48568d --- /dev/null +++ b/reports/figures/results/factors.csv @@ -0,0 +1,1442 @@ +Time,Factor_Pandemic,Factor_Consumption,Factor_GDP,Factor_Cons3,Factor_Cons4,Factor_Cons5,Factor_Cons2,Factor_Cases5,Factor_Cases2,Factor_Deaths5,Factor_Deaths2,Factor_Cases3,Factor_Deaths3,Factor_Cases4,Factor_Cases1,Factor_Hosp2,Factor_Hosp1,Factor_Deaths4,Factor_Deaths1,State +2/1/2012,-1.161054658334526,0.1700950178211212,294888.4089,22717.46446,43092.70514,135937.3315,65810.18635,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +3/1/2012,-1.1712219382075233,-0.0254673278052625,295976.4376,22726.87622,43088.33409,136033.213,65815.23537,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN 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+2/1/2018,-1.1718619866751547,-0.1088585158734872,337652.2184,27590.94077,42073.01587,158815.5633,69663.94115,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +3/1/2018,-1.1718619866751545,-0.843759338713566,339814.2461,27677.68169,42166.67285,159209.4556,69844.33135,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +4/1/2018,-1.1718619866751552,-0.4343185730784811,340651.6773,27749.00915,42236.97695,159515.0387,69985.95523,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +5/1/2018,-1.171861986675154,-0.4625526593571707,341504.78,27821.60121,42309.35304,159828.293,70130.91574,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +6/1/2018,-1.1718619866751538,-0.9920715008413972,342610.6705,27914.77792,42413.14844,160260.1161,70327.8802,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +7/1/2018,-1.1718619866751534,-1.1388735396395502,343593.9013,28013.94154,42526.11829,160726.5235,70540.00603,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +8/1/2018,-1.1718619866751558,-1.225312539792173,344622.9563,28116.80891,42644.7746,161214.3555,70761.52206,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +9/1/2018,-1.1718619866751547,-0.2051369041418651,345170.4096,28180.32794,42703.86158,161476.8756,70884.12055,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +10/1/2018,-1.171861986675155,-0.5272190648414683,345115.5852,28256.53667,42782.32223,161812.5023,71038.78239,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +11/1/2018,-1.1718619866751532,-1.452306182291076,345507.4379,28369.14627,42915.97457,162356.7941,71285.03672,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +12/1/2018,-1.171861986675154,-1.5915798269607293,345971.961,28487.67329,43058.60117,162935.013,71546.18271,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +1/1/2019,-1.1718619866751534,-0.02376025134341,345641.3127,28554.89385,43098.47491,163272.3648,71653.28464,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +2/1/2019,-1.1718619866751545,1.1952598545051707,344731.6133,28574.14228,43066.03082,163335.4864,71640.09675,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +3/1/2019,-1.1718619866751532,2.0443497866616056,343422.6035,28559.88962,42983.35663,163207.3088,71543.17773,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +4/1/2019,-1.1718619866751534,2.305790498638025,343834.7908,28535.31305,42885.49827,163020.4044,71420.75059,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +5/1/2019,-1.1718619866751545,0.5829817513165556,345062.324,28578.62414,42889.89677,163221.5127,71468.46781,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +6/1/2019,-1.1718619866751545,0.6055684041004695,346278.7101,28621.09428,42893.11866,163417.8833,71514.16746,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +7/1/2019,-1.1718619866751538,1.134747365860839,347868.3973,28642.67505,42865.16522,163495.0815,71507.80242,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +8/1/2019,-1.171861986675154,0.3407913017241397,349837.4375,28695.68639,42884.35695,163751.7706,71580.01308,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +9/1/2019,-1.1718619866751545,0.5869461823839492,351687.0072,28739.00932,42889.13136,163953.2198,71628.118,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +10/1/2019,-1.171861986675154,1.5150140001057526,352250.437,28745.52101,42839.12772,163944.7851,71584.63364,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +11/1/2019,-1.1718619866751545,0.4011303387995145,353354.6063,28796.25355,42855.16751,164188.6638,71651.41352,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +12/1/2019,-1.1718619866751532,1.8508158889400264,353750.4515,28789.37292,42785.6343,164104.1757,71575.00722,0.0,0.0,0.0,0.0,0.0,,,,,,,,MN +1/1/2020,-1.171861986675154,0.9861317303098502,351659.6661,28863.18773,42721.44055,162777.9856,71584.62828,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,MN +2/1/2020,-1.171828268928335,0.4534595843512705,349834.7889,28958.08949,42688.97992,161575.9581,71647.06942,0.0,0.0,0.0,0.0,0.0,0.0,32.0,32.0,8.0,8.0,0.0,0.0,MN +3/1/2020,-1.1074042433183546,-1.97011914621256,349182.2168,29150.29687,42799.77712,160914.9868,71950.07399,689.0,689.0,17.0,17.0,629.0,12.0,2496.0,2486.0,644.0,644.0,56.0,56.0,MN +4/1/2020,-0.7413689057190762,-4.205125062980562,341846.7181,29434.12829,43043.80562,160746.785,72477.93391,4447.0,4447.0,326.0,326.0,4507.0,331.0,16598.0,16580.0,2500.0,2494.0,966.0,966.0,MN +5/1/2020,0.6906451089990373,0.4089479464891266,332302.1878,29531.66203,43014.66657,159553.8333,72546.3286,20072.0,20072.0,717.0,707.0,19714.0,707.0,36446.0,36424.0,3922.0,3920.0,986.0,986.0,MN +6/1/2020,-0.1139969130005269,2.226508821542537,321970.3646,29554.88234,42878.07235,157967.2855,72432.95469,11508.0,11508.0,422.0,395.0,11453.0,426.0,23430.0,23430.0,1686.0,1686.0,25.0,25.0,MN +7/1/2020,0.5309734525443854,1.783343138563086,329122.4911,29595.78021,42768.22692,156486.2957,72364.00713,18472.0,18472.0,164.0,161.0,18160.0,164.0,40386.0,40382.0,2338.0,2338.0,0.0,0.0,MN +8/1/2020,0.7634876767531077,1.8651201357467344,336193.5237,29632.98059,42654.04673,154995.8861,72287.02731,20676.0,20676.0,220.0,211.0,21401.0,226.0,41780.0,41772.0,2582.0,2580.0,138.0,138.0,MN +9/1/2020,0.992892273181646,0.9733793780953364,343642.9448,29706.37381,42592.77593,153702.9513,72299.14974,23270.0,23270.0,223.0,219.0,23270.0,223.0,52572.0,51884.0,2891.0,2889.0,266.0,256.0,MN +10/1/2020,3.4184132741725373,-0.1075010773287692,345657.2143,29824.04521,42595.3443,152642.6656,72419.38951,49338.0,48697.0,422.0,402.0,49338.0,422.0,124325.0,121061.0,6547.0,6501.0,861.0,809.0,MN +11/1/2020,14.618970938229396,-0.2137578243193527,347723.1506,29946.20117,42604.39615,151606.1215,72550.59732,170291.0,164262.0,1141.0,1083.0,170291.0,1141.0,366503.0,350488.0,14735.0,14441.0,3132.0,2906.0,MN +12/1/2020,7.712050864855097,2.8060658742921647,348334.9978,29943.3794,42436.37985,149944.2711,72379.75925,96539.0,88865.0,1730.0,1630.0,96539.0,1730.0,157830.0,145014.0,8282.0,7774.0,1556.0,1396.0,MN +1/1/2021,3.102255045949692,-4.618314473102906,349315.0535,30234.84896,42718.2834,150710.3426,72953.13236,46505.0,40820.0,886.0,801.0,46505.0,886.0,76766.0,66562.0,4009.0,3735.0,243.0,227.0,MN +2/1/2021,0.9265461351287824,-4.880533535757382,350458.1522,30538.94707,43018.9623,151544.294,73557.90937,22787.0,19225.0,283.0,260.0,22787.0,283.0,44225.0,37332.0,2375.0,2217.0,0.0,0.0,MN +3/1/2021,1.9998847544067293,-2.919871087896867,350687.0424,30761.49324,43205.94111,151979.79,73967.43435,34044.0,27932.0,237.0,123.0,34935.0,375.0,82169.0,68447.0,3986.0,3648.0,22.0,22.0,MN +4/1/2021,4.025888653492563,-3.2612264818074115,352789.9356,30999.05461,43415.41344,152496.8925,74414.46804,56283.0,47210.0,311.0,278.0,56283.0,311.0,109725.0,91789.0,5864.0,5370.0,24.0,24.0,MN +5/1/2021,1.1869537081097306,-3.3454183978030607,354947.9035,31240.90961,43632.17325,153041.8528,74873.08285,25638.0,21035.0,282.0,265.0,25572.0,282.0,39451.0,32889.0,2804.0,2640.0,0.0,0.0,MN +6/1/2021,-0.8047664581238809,-2.908233085768065,356904.7619,31464.51523,43824.72414,153504.1747,75289.23938,3915.0,3263.0,168.0,158.0,3981.0,168.0,8004.0,6722.0,1006.0,964.0,0.0,0.0,MN +7/1/2021,-0.4773039697053704,-3.533402980788915,356240.6095,31715.98296,44057.27846,154108.7502,75773.26143,7543.0,6194.0,76.0,73.0,7336.0,74.0,22234.0,18757.0,1760.0,1645.0,0.0,0.0,MN +8/1/2021,2.26126665013728,-3.895757556452589,355796.234,31984.38734,44314.34853,154800.8485,76298.73587,37056.0,32739.0,136.0,130.0,37263.0,143.0,83623.0,73762.0,4625.0,4425.0,32.0,32.0,MN +9/1/2021,4.469581956692372,-4.403476978708225,355641.3088,32276.64201,44605.27104,155612.6507,76881.91305,61130.0,51189.0,296.0,250.0,61130.0,352.0,138105.0,116361.0,6064.0,5632.0,209.0,195.0,MN +10/1/2021,5.882209073236569,-2.431039804979513,356688.4862,32481.9755,44777.06654,156010.6487,77259.04204,76456.0,64352.0,537.0,478.0,76456.0,537.0,156649.0,130449.0,6848.0,6426.0,469.0,419.0,MN +11/1/2021,9.83842665838088,-2.0661978684564097,357572.4478,32671.15059,44927.89763,156337.9653,77599.04822,116866.0,93663.0,713.0,661.0,124820.0,713.0,246831.0,196352.0,10390.0,9565.0,1044.0,943.0,MN +12/1/2021,9.03851218505931,-2.500345211905098,358686.9851,32880.15079,45107.24413,156766.7724,77987.39492,109842.0,86499.0,1150.0,1045.0,109842.0,1150.0,268807.0,212654.0,9325.0,8702.0,540.0,494.0,MN +1/1/2022,26.38915678235739,-0.3009135825214599,358706.4148,32867.43841,45272.38586,156996.0617,78139.82428,299541.0,240471.0,905.0,1539.0,299541.0,905.0,608613.0,489238.0,12833.0,12363.0,800.0,697.0,MN +2/1/2022,7.280109762214207,0.2055426560560211,358483.8601,32832.71481,45405.17677,157116.9848,78237.89157,91823.0,74930.0,728.0,698.0,91823.0,728.0,92503.0,77119.0,4088.0,3990.0,37.0,29.0,MN +3/1/2022,0.3174412988316692,2.8134122023654697,356982.3031,32681.06923,45373.51324,156674.0094,78054.58247,15974.0,13353.0,349.0,299.0,15974.0,349.0,25862.0,21677.0,1631.0,1611.0,0.0,0.0,MN +4/1/2022,0.9531471573952232,-2.5632194676252587,359419.9715,32773.37677,45678.50963,157397.3166,78451.8864,22997.0,19111.0,140.0,100.0,22997.0,140.0,56012.0,46775.0,2102.0,2052.0,0.0,0.0,MN +5/1/2022,3.861499559637124,0.4084577681160821,360383.4015,32732.29763,45796.18425,157477.78,78528.48188,54621.0,45245.0,165.0,125.0,54621.0,165.0,115062.0,94381.0,4322.0,4155.0,34.0,28.0,MN +6/1/2022,3.034064831977589,3.20875493354945,359945.6733,32565.98549,45735.87991,156951.3489,78301.86541,45672.0,37328.0,184.0,173.0,45672.0,184.0,85563.0,69933.0,3873.0,3759.0,11.0,8.0,MN +7/1/2022,2.309703059369752,-4.573788116727272,363169.1208,32751.36349,46167.95114,158117.438,78919.31462,37724.0,30444.0,166.0,147.0,37724.0,166.0,83071.0,66893.0,4237.0,4122.0,0.0,0.0,MN +8/1/2022,2.534764694975244,-1.922748688376818,365077.832,32818.35963,46432.84732,158711.5285,79251.20695,40255.0,32712.0,136.0,125.0,40255.0,136.0,73590.0,60023.0,3903.0,3810.0,26.0,16.0,MN +9/1/2022,1.9428139068359824,-1.338609789655672,366695.5555,32859.83537,46660.59893,159180.554,79520.4343,33978.0,27981.0,222.0,207.0,33978.0,222.0,26597.0,22073.0,1944.0,1887.0,19.0,15.0,MN +10/1/2022,-0.7905311901487764,-1.0628162603813212,366365.2777,32889.67526,46870.61511,159591.2873,79760.29037,5968.0,5025.0,44.0,40.0,0.0,0.0,23194.0,19166.0,2043.0,1970.0,33.0,20.0,MN +11/1/2022,-1.2091971778076225,-3.0570636117941308,367058.616,33011.12679,47210.39355,160445.292,80221.52034,0.0,0.0,0.0,0.0,0.0,,23433.0,20035.0,2230.0,2173.0,85.0,53.0,MN +12/1/2022,-1.1622831146863413,-3.348836367079479,367915.6151,33147.20894,47570.84826,161370.0134,80718.05719,0.0,0.0,0.0,0.0,0.0,,24060.0,20362.0,2422.0,2372.0,70.0,37.0,MN +2/1/2012,1.1218847831365997,0.6174203110345873,718953.5051,46458.56983,97025.18124,315340.4658,143483.7511,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +3/1/2012,1.1799605565125115,0.4658808211848997,722927.0567,46470.07069,96987.04066,315373.4897,143457.1113,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +4/1/2012,1.1974497833326805,0.1471295836336736,724230.836,46508.98871,97006.3218,315592.7276,143515.3105,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +5/1/2012,1.2023242119620872,-1.3041832129832005,727298.5879,46661.19487,97261.90596,316580.7052,143923.1008,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +6/1/2012,1.2037036705776067,-0.881581895946064,729919.0604,46784.63251,97457.42397,317373.4253,144242.0565,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +7/1/2012,1.2040971291742912,-0.3128486710624245,730136.7556,46867.74781,97568.93757,317892.6041,144436.6854,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +8/1/2012,1.2042096389950616,1.5613579895888705,728172.1709,46810.52544,97388.52527,317460.0716,144199.0507,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +9/1/2012,1.2042418354011808,1.3996235613279313,726444.9736,46768.12381,97239.33791,317128.3336,144007.4617,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +10/1/2012,1.204251050890691,1.3903752820328563,723774.9767,46728.76294,97096.83774,316817.479,143825.6007,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +11/1/2012,1.2042536887790585,-0.813593909826122,723744.0653,46859.48408,97307.88664,317659.8681,144167.3707,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +12/1/2012,1.2042544438750216,-0.4665279783543528,723338.9997,46966.09108,97468.80996,318338.7512,144434.901,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +1/1/2013,1.2042546600224624,0.8588102460969749,725280.5752,47001.86201,97395.299,318377.5768,144397.1693,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +2/1/2013,1.2042547218951074,1.9463063369180225,725956.6083,46956.12058,97153.70319,317865.3927,144109.8402,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +3/1/2013,1.2042547396062853,-1.0808601682455006,730238.4605,47143.81873,97395.4446,318933.9309,144539.2881,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +4/1/2013,1.204254744676149,-0.7292055296763804,730051.2165,47306.6915,97585.59201,319834.0994,144892.3165,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +5/1/2013,1.2042547461274085,0.1753487011418528,728815.9588,47401.6764,97635.67964,320275.2615,145037.3973,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +6/1/2013,1.2042547465428348,1.119385535647979,726489.8052,47425.21832,97539.02082,320234.2731,144964.2885,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +7/1/2013,1.2042547466617513,0.5795712043236234,727108.8133,47491.72866,97531.22718,320484.1036,145023.0134,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +8/1/2013,1.2042547466957916,0.7794121130061309,727511.214,47544.01865,97494.63107,320638.5249,145038.7154,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +9/1/2013,1.2042547467055358,0.0951181960002165,728743.87,47650.51699,97569.47317,321158.9119,145220.064,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +10/1/2013,1.2042547467083249,0.9097978920033752,731210.3576,47694.95946,97517.54783,321261.461,145212.5892,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +11/1/2013,1.2042547467091231,0.8461077477939973,733762.1343,47745.34342,97478.23441,321404.6698,145223.6678,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +12/1/2013,1.204254746709352,1.1249723073982114,735988.5976,47774.96963,97397.04557,321408.8398,145172.1132,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +1/1/2014,1.2042547467094171,0.9781190510839064,734536.101,47783.81796,97374.44351,321897.3696,145158.3511,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL +2/1/2014,1.204254746709436,0.1867518337316835,734049.001,47855.21181,97479.39833,322806.175,145334.6916,0.0,0.0,0.0,0.0,0.0,,,,,,,,IL 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+12/1/2013,0.8500476352643505,0.8082074104492833,238347.4394,15746.9339,31197.05341,108327.4558,46943.9873,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +1/1/2014,0.8500476352643512,0.1155821328987674,236843.4724,15749.80282,31237.84359,108472.2765,46987.64641,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +2/1/2014,0.8500476352643513,-0.8586368214141205,235653.3578,15773.28721,31319.43494,108758.77,47092.72216,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +3/1/2014,0.8500476352643502,0.2114219235008716,234133.0954,15774.40683,31356.52848,108890.7336,47130.93531,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +4/1/2014,0.8500476352643503,0.1734004670423764,235105.0852,15776.44326,31395.30734,109028.5375,47171.7506,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +5/1/2014,0.8500476352643515,0.0189049035252273,236123.7802,15781.84462,31440.6556,109189.1439,47222.50022,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT +6/1/2014,0.8500476352643501,-0.4187574017722315,237279.3711,15796.57398,31504.48877,109413.937,47301.06275,0.0,0.0,0.0,0.0,0.0,,,,,,,,CT 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+7/1/2021,-0.3923818461375098,-3.750081816829415,723121.986,61464.07038,112692.085,351938.438,174156.1194,13522.0,10359.0,174.0,197.0,14205.0,172.0,35228.0,27615.0,1757.0,1666.0,0.0,0.0,PA +8/1/2021,2.4949013279878405,-4.064163976503597,725233.6173,62136.84976,113363.2762,352948.4845,175500.0973,69312.0,53469.0,383.0,810.0,71310.0,385.0,159027.0,125754.0,5089.0,4699.0,213.0,189.0,PA +9/1/2021,5.34259895379555,-4.509320463320828,727914.0127,62854.89864,114120.972,354235.4548,176975.8493,129572.0,103163.0,1165.0,997.0,132222.0,1165.0,280026.0,225717.0,6953.0,6519.0,826.0,730.0,PA +10/1/2021,4.935871899726112,-2.7254997182720726,732999.4581,63402.36695,114573.7678,354585.3869,177976.1206,134999.0,108533.0,2054.0,1496.0,137297.0,2052.0,262509.0,210943.0,7936.0,7383.0,1168.0,1062.0,PA +11/1/2021,7.027907797853299,-2.386977148748514,737714.9464,63916.56796,114972.7651,354781.2042,178889.326,155026.0,118632.0,1967.0,1587.0,155986.0,1969.0,354302.0,282308.0,9154.0,8455.0,2088.0,1888.0,PA +12/1/2021,15.945435082862518,-2.766971453544302,742872.9984,64467.90249,115444.5856,355213.6047,179912.4881,299504.0,244434.0,3284.0,2786.0,299504.0,3284.0,730634.0,606136.0,13514.0,12830.0,3805.0,3564.0,PA +1/1/2022,25.242956368182057,-1.0670171626375191,739490.3001,64444.49327,116023.843,356077.7412,180468.3433,625057.0,532664.0,3876.0,3196.0,625057.0,3876.0,1115202.0,925817.0,16089.0,15501.0,4313.0,3967.0,PA +2/1/2022,2.2337219804538493,-0.6002924365221983,735646.8183,64377.90888,116518.456,356692.2676,180896.3787,95555.0,78955.0,2635.0,2571.0,95944.0,2635.0,146591.0,117605.0,4537.0,4356.0,145.0,129.0,PA +3/1/2022,-0.2351587665699409,1.755096957129608,729230.3888,64082.04268,116588.6986,356021.4225,180670.7619,24317.0,18717.0,1059.0,1188.0,23928.0,1059.0,42010.0,32627.0,1611.0,1538.0,0.0,0.0,PA +4/1/2022,0.7522584817358362,-3.066751478034918,733908.5632,64264.51031,117522.2724,357996.9782,181786.8101,38866.0,30780.0,402.0,287.0,38866.0,402.0,84356.0,66519.0,1888.0,1801.0,0.0,0.0,PA +5/1/2022,4.0806440194659785,-0.3850218672436194,735578.8498,64185.41156,117972.7105,358507.7542,182158.1562,105433.0,83653.0,353.0,243.0,87105.0,470.0,228822.0,178123.0,4648.0,4441.0,111.0,105.0,PA +6/1/2022,2.1807066944325344,2.142571861804449,734393.905,63860.71799,117962.3437,357631.7281,181823.1022,80605.0,63270.0,582.0,372.0,98933.0,619.0,146742.0,114679.0,3577.0,3437.0,47.0,41.0,PA +7/1/2022,2.940719018053398,-4.84253741581673,739165.4308,64225.66324,119220.6253,360609.4286,183446.3358,83892.0,67292.0,439.0,338.0,74336.0,398.0,180148.0,141101.0,3978.0,3837.0,34.0,32.0,PA +8/1/2022,2.304951928068097,-2.449815118399622,741264.1709,64358.45834,120046.9578,362282.0488,184405.4701,95753.0,75145.0,511.0,584.0,105309.0,552.0,152316.0,116802.0,2919.0,2753.0,48.0,41.0,PA +9/1/2022,0.4485767275239769,-1.9150554685834769,742782.9148,64441.19803,120776.3946,363667.26,185217.6531,68601.0,53211.0,497.0,347.0,68601.0,497.0,70589.0,53420.0,1698.0,1600.0,19.0,19.0,PA +10/1/2022,0.0400454206180934,-1.6577200548219542,742953.6816,64501.10868,121458.9201,364917.0431,185960.0956,12431.0,9877.0,110.0,89.0,0.0,0.0,55166.0,41252.0,1669.0,1590.0,60.0,48.0,PA +11/1/2022,-0.15169749586964,-3.441013367169263,745196.2686,64740.67557,122477.053,367178.9291,187217.8021,0.0,0.0,0.0,0.0,0.0,,48504.0,36869.0,1456.0,1373.0,41.0,35.0,PA +12/1/2022,0.1445042684481936,-3.694296448868446,747769.8724,65008.93365,123548.6223,369602.1921,188557.6361,0.0,0.0,0.0,0.0,0.0,,62529.0,49042.0,1887.0,1808.0,90.0,78.0,PA +2/1/2012,1.0277863077790517,0.9180016130090688,437236.0574,27727.81154,58369.04583,203748.874,86096.85737,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +3/1/2012,1.0234465029518196,0.7081943357273119,437479.8215,27743.14433,58353.48674,203852.9854,86096.63107,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +4/1/2012,1.018687205607272,0.2934480983649441,438989.9799,27774.81826,58372.45242,204077.1968,86147.27068,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +5/1/2012,1.0135030727655334,-1.5145970141755136,441569.227,27874.1454,58533.60935,204798.496,86407.75474,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +6/1/2012,1.0078931876126191,-1.0136163758234171,443878.0791,27956.30065,58658.62954,205393.6151,86614.93019,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +7/1/2012,1.0018614700069206,-0.3316770673563716,444952.2759,28014.36234,58733.08937,205811.7188,86747.45171,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +8/1/2012,0.9954169864515654,1.9589353549322064,444693.0802,27988.50862,58631.79282,205613.3566,86620.30144,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +9/1/2012,0.9885741356837716,1.743158582120183,444572.8688,27971.46315,58549.24835,205479.7567,86520.7115,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +10/1/2012,0.9813526901006696,1.7161260807076173,443345.4844,27956.18662,58470.68678,205359.2015,86426.8734,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +11/1/2012,0.9737776792456554,-1.0127129080887982,443733.7283,28042.64529,58605.01214,205985.987,86647.65743,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +12/1/2012,0.9658791092211554,-0.5990521648730576,443892.915,28114.67995,58709.15471,206506.8146,86823.83466,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +1/1/2013,0.9576915206628088,0.9699010377242518,443749.723,28128.98324,58693.24251,206657.6047,86822.22575,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +2/1/2013,0.9492533971165504,2.2969319200151936,442839.6404,28094.54374,58575.8795,206450.0376,86670.42324,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +3/1/2013,0.9406064445055262,-1.4423389717489885,444132.2273,28199.79114,58749.86389,207268.8398,86949.65503,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +4/1/2013,0.9317947700476618,-1.020059322112154,445150.2153,28290.17409,58892.78544,207978.4831,87182.95953,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +5/1/2013,0.9228639947692964,0.0837446160785413,445529.3205,28339.95809,58951.1847,208389.6644,87291.14279,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +6/1/2013,0.9138603371270428,1.2366557659662336,445238.3728,28347.04825,58920.90133,208486.7823,87267.94958,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +7/1/2013,0.9048297059294138,0.5628176122053198,446672.0274,28379.84523,58944.20381,208772.8161,87324.04904,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +8/1/2013,0.8958168387623623,0.8003676536611233,447969.691,28404.16379,58950.02134,208996.355,87354.18514,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +9/1/2013,0.8868645177665521,-0.0503300828779548,449776.5633,28460.88134,59023.16593,209458.1979,87484.04727,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +10/1/2013,0.8780128884115903,0.9454456034527898,450460.1758,28480.54829,59019.5664,209647.2739,87500.11469,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +11/1/2013,0.869298899530355,0.8598930964715952,451199.0459,28503.78546,59023.50945,209862.4866,87527.29492,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +12/1/2013,0.8607558750318927,1.196585876915278,451740.8213,28514.6546,59001.99835,209986.4817,87516.65295,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +1/1/2014,0.8524132200820217,0.502558756123964,449677.9366,28543.75656,59043.66042,210352.2181,87587.41697,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +2/1/2014,0.8442962576932855,-0.4753116491544148,448208.9742,28610.1611,59162.53005,210992.4792,87772.69115,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +3/1/2014,0.8364261860089999,0.608581057407725,446110.0221,28635.93809,59197.43425,211332.7336,87833.37234,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +4/1/2014,0.828820142333158,0.5745866836069143,447498.978,28663.28603,59235.65825,211683.9916,87898.94428,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +5/1/2014,0.8214913572142234,0.4229312664462801,448978.5171,28696.66113,59286.40484,212079.216,87983.06597,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +6/1/2014,0.8144493805601543,-0.0144981483862295,450719.6584,28746.93065,59372.10657,212598.8742,88119.03722,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +7/1/2014,0.8077003616648448,0.2824000046844455,453541.0408,28786.20344,59435.14391,213036.8172,88221.34735,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +8/1/2014,0.8012473659198207,-0.9381127902756912,457097.0274,28872.29389,59594.87095,213821.0389,88467.16484,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +9/1/2014,0.7950907126025754,-0.6409098469692753,460479.831,28947.46277,59732.05721,214524.3598,88679.51998,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +10/1/2014,0.7892283202130628,-1.011024181430057,463685.2529,29037.22083,59899.34772,215335.7984,88936.56855,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +11/1/2014,0.7836560481399863,-1.389065019885728,467132.3078,29141.99758,60097.60131,216258.7619,89239.59889,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +12/1/2014,0.7783680257868263,-1.959261216193227,470945.7937,29269.38022,60342.43248,217349.8154,89611.81271,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +1/1/2015,0.7733569625286868,-3.330502109408997,474958.4411,29495.16908,60654.31285,219183.3634,90149.48194,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +2/1/2015,0.7686144339051633,1.419796652073554,476002.8191,29536.55709,60586.53403,219647.063,90123.09111,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +3/1/2015,0.7641311412220291,1.6290131420202698,476913.8684,29569.75523,60502.49925,220049.3042,90072.25448,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +4/1/2015,0.759897143209583,0.7646179142942309,479180.8052,29636.76999,60488.064,220702.7824,90124.834,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +5/1/2015,0.7559020595675627,1.9944355186330776,480664.8506,29655.59444,60375.74967,220996.9044,90031.34411,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +6/1/2015,0.7521352471328422,1.6582035895925742,482356.4186,29687.53628,60290.79105,221388.1149,89978.32733,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +7/1/2015,0.7485859500657053,0.8196109212426602,482421.6059,29752.39509,60273.09449,222024.3746,90025.48957,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +8/1/2015,0.745243425897509,0.2096631379481031,482878.4891,29841.36038,60304.35735,222840.4001,90145.71773,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +9/1/2015,0.7420970495537973,-0.9126963457694738,484055.9982,29974.94893,60425.57775,223989.8731,90400.52668,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +10/1/2015,0.7391363975973937,0.1011412031600361,486221.8629,30068.70074,60466.25159,224841.9092,90534.95233,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +11/1/2015,0.7363513149580556,0.6761614168455976,488019.7154,30139.71278,60461.26589,225523.8363,90600.97867,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +12/1/2015,0.7337319663573354,-0.39627045532078,490512.7212,30253.6488,60542.35712,226526.9774,90796.00593,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +1/1/2016,0.7312688745232191,0.1426312354837811,490085.8614,30306.59723,60620.31731,227144.0026,90926.91454,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +2/1/2016,0.7289529471384555,-0.7090282889883871,490214.3507,30393.89654,60766.98437,228018.5067,91160.88091,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +3/1/2016,0.7267754942949325,1.513374676877338,488907.239,30392.05265,60735.47318,228223.8768,91127.52583,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +4/1/2016,0.724728238045653,2.2644810162634954,489427.6156,30360.06794,60643.8843,228201.683,91003.95224,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +5/1/2016,0.7228033154649176,0.8944768827643903,490830.4401,30383.1296,60662.38207,228592.2047,91045.51167,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA 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+5/1/2019,0.6951157238160826,0.0283975123869311,529673.3906,31501.1242,63911.4462,245711.803,95412.62349,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +6/1/2019,0.6948861060737349,0.053004099798223,531866.4665,31529.94952,64035.79388,246225.9972,95565.78888,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +7/1/2019,0.6946684051665366,0.606634340652273,533879.291,31535.80122,64113.34102,246560.1592,95649.18009,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +8/1/2019,0.6944619709076517,-0.221193096085106,536475.0784,31576.29027,64261.1896,247164.6146,95837.51014,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +9/1/2019,0.6942661912571118,0.0369359099884701,538888.0924,31606.13663,64387.31206,247685.4692,95993.47138,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +10/1/2019,0.6940804898775274,1.007114628374088,539572.2811,31595.54622,64430.88547,247888.6796,96026.44678,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA +11/1/2019,0.6939043238599645,-0.1546355297400197,541085.1913,31633.60309,64573.5078,248472.8823,96207.11844,0.0,0.0,0.0,0.0,0.0,,,,,,,,MA 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+12/1/2012,1.1271301781870973,-0.80759300183827,1354369.191,66074.9754,158048.0749,538874.4744,224123.0503,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +1/1/2013,1.1271301780691023,0.5822919601919287,1343561.041,66166.43681,158161.5354,539491.9566,224327.9639,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +2/1/2013,1.1271301780837917,2.008037284433754,1330488.684,66142.92116,158000.7447,539172.6805,224143.6494,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +3/1/2013,1.1271301780819627,-2.0585694163574533,1324058.284,66448.13557,158625.4615,541533.3884,225073.5724,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +4/1/2013,1.127130178082191,-1.612226011899936,1330764.351,66718.44455,159166.6336,543609.361,225885.0452,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +5/1/2013,1.1271301780821563,-0.4259933891726988,1335560.968,66893.01389,159479.3915,544905.2487,226372.3641,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +6/1/2013,1.1271301780821652,0.8141933758718075,1338338.701,66966.64954,159551.8038,545379.2923,226518.404,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +7/1/2013,1.127130178082162,0.0744419020788065,1342243.687,67100.82154,159768.7972,546346.773,226869.5611,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +8/1/2013,1.127130178082162,0.3235413014541688,1345741.142,67214.79,159937.97,547150.1002,227152.6943,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +9/1/2013,1.1271301780821648,-0.606682045040128,1350769.079,67405.31758,160289.5126,548576.8833,227694.7563,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +10/1/2013,1.1271301780821628,0.4657185592070916,1357112.227,67507.97926,160432.3221,549288.8297,227940.2194,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +11/1/2013,1.1271301780821623,0.366245737096082,1363607.935,67618.92142,160595.1388,550068.5522,228213.9702,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +12/1/2013,1.1271301780821643,0.7251420289318314,1369492.869,67700.32522,160688.1588,550608.4205,228388.386,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +1/1/2014,1.1271301780821674,-0.2823467192433164,1367096.476,67803.91026,161040.2938,551899.4717,228844.1144,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +2/1/2014,1.1271301780821648,-1.3446736306121263,1366496.088,67996.01973,161602.4188,553909.9683,229598.3571,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +3/1/2014,1.1271301780821716,-0.1710553852444232,1363964.971,68091.48357,161934.5787,555132.0178,230025.9891,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +4/1/2014,1.1271301780821588,-0.2091308489145703,1367891.06,68190.54849,162274.8892,556381.681,230465.3728,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +5/1/2014,1.1271301780821623,-0.3744666245957208,1372095.208,68303.82793,162648.6444,557745.711,230952.4157,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +6/1/2014,1.1271301780821696,-0.8493185609365888,1377099.723,68457.22327,163117.6299,559436.0654,231574.8047,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +7/1/2014,1.1271301780821663,-0.5276002404797399,1383134.025,68584.3428,163523.7345,560910.5453,232108.037,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +8/1/2014,1.1271301780821614,-1.8509051020751852,1391412.136,68822.96119,164195.541,563296.3096,233018.4699,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +9/1/2014,1.127130178082163,-1.528281072747879,1399163.396,69035.5416,164805.214,565468.9059,233840.7314,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +10/1/2014,1.127130178082164,-1.9289525943157104,1405392.901,69282.91512,165497.9469,567926.492,234780.8459,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +11/1/2014,1.12713017808216,-2.3379793451438804,1412351.226,69566.15712,166276.4635,570678.5372,235842.6125,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +12/1/2014,1.1271301780821588,-2.9551221600454194,1420409.462,69903.4389,167184.3793,573874.8786,237087.8181,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +1/1/2015,1.1271301780821543,-3.3808034359370485,1429268.205,70333.62612,168129.8411,578209.0714,238463.4591,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +2/1/2015,1.1271301780821628,1.7650312492643518,1429185.098,70323.77185,168023.1535,578928.0843,238346.9091,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +3/1/2015,1.1271301780821654,1.9871720142120672,1428713.131,70294.8041,167871.0943,579486.9008,238165.8741,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY 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+10/1/2018,1.1271301780821723,-0.2617442438685147,1583642.662,73063.68049,175218.501,634713.271,248282.1815,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +11/1/2018,1.1271301780821603,-1.3131482481904395,1581707.152,73304.78546,175790.8212,636917.5561,249095.6067,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +12/1/2018,1.1271301780821648,-1.4748888301458958,1580106.614,73561.21148,176399.8862,639254.8926,249961.0977,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +1/1/2019,1.1271301780821643,-1.285840737982454,1591398.736,73719.92867,177051.8167,641168.9317,250771.7454,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +2/1/2019,1.1271301780821608,0.0982103260456241,1600006.597,73754.81771,177405.9669,642005.2346,251160.7846,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +3/1/2019,1.1271301780821683,1.063585286412749,1606706.505,73703.29761,177551.1225,642087.1138,251254.4201,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +4/1/2019,1.127130178082166,1.3639204607841044,1612276.219,73625.22014,177630.7463,641934.5661,251255.9665,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +5/1/2019,1.1271301780821663,-0.582177748407815,1621655.501,73722.35775,178132.0926,643307.7241,251854.4504,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +6/1/2019,1.127130178082159,-0.5523937240169345,1630977.305,73817.34661,178627.871,644661.3908,252445.2176,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +7/1/2019,1.1271301780821632,0.051072076294104,1632588.031,73858.49062,178992.7774,645543.6869,252851.268,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +8/1/2019,1.127130178082168,-0.8434283601786108,1635989.57,73980.708,179553.6871,647133.6517,253534.3951,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +9/1/2019,1.127130178082164,-0.5604502400503332,1638838.368,74077.96238,180053.7398,648504.7229,254131.7022,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +10/1/2019,1.1271301780821683,0.4945193366917424,1638462.186,74080.36973,180322.5626,649044.1001,254402.9324,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +11/1/2019,1.127130178082165,-0.7621291943280089,1640608.315,74196.77315,180868.2451,650581.0076,255065.0182,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +12/1/2019,1.1271301780821674,0.8834080587034605,1639474.155,74164.77026,181051.4196,650815.1821,255216.1899,0.0,0.0,0.0,0.0,0.0,,,,,,,,NY +1/1/2020,1.094009688411116,2.608203639959214,1639370.313,74149.95403,180682.3412,646355.5187,254832.2953,0.0,0.0,0.0,0.0,0.0,0.0,1051.0,1027.0,112.0,110.0,0.0,0.0,NY +2/1/2020,0.624962537191914,1.9997910140230308,1640477.087,74189.92527,180447.7657,642386.453,254637.6909,0.0,0.0,0.0,0.0,0.0,0.0,2099.0,1959.0,1705.0,1587.0,141.0,106.0,NY +3/1/2020,-15.63887727028833,-0.749294226217363,1647091.605,74478.97116,180818.6404,640569.3713,255297.6115,32656.0,32656.0,211.0,211.0,75853.0,2767.0,259565.0,256294.0,56660.0,56096.0,19272.0,17975.0,NY +4/1/2020,-20.289021071857075,-3.284830606262603,1605812.688,75000.58964,181751.6502,640720.0547,256752.2399,104238.0,104238.0,5134.0,5134.0,232456.0,21667.0,445784.0,407230.0,72419.0,71116.0,33508.0,26319.0,NY +5/1/2020,-2.6745741896478954,1.9333856567271743,1554127.814,75046.64576,181530.7996,636789.6961,256577.4454,30573.0,30573.0,2785.0,2785.0,65373.0,6129.0,268897.0,165444.0,12863.0,12534.0,3471.0,2740.0,NY +6/1/2020,-0.3532788937115383,3.9860572785974817,1498793.275,74904.78211,180856.9158,631284.6514,255761.6979,10808.0,10808.0,550.0,550.0,22227.0,1346.0,136776.0,83759.0,5007.0,4906.0,323.0,314.0,NY +7/1/2020,-0.0784081837233675,3.479620917649449,1530233.122,74809.02028,180296.4894,626196.041,255105.5096,11591.0,11591.0,161.0,161.0,22235.0,599.0,117869.0,85984.0,4075.0,4022.0,66.0,66.0,NY +8/1/2020,0.185147053473557,3.5675227201086344,1561306.753,74705.10093,179718.3701,621064.7363,254423.4711,10653.0,10653.0,111.0,111.0,19010.0,283.0,88262.0,75862.0,3184.0,3144.0,72.0,70.0,NY +9/1/2020,0.1447575316982449,2.5533856842435894,1594146.202,74693.38249,179363.6494,616720.6329,254057.0319,14089.0,14089.0,89.0,89.0,24383.0,367.0,95724.0,93055.0,3320.0,3285.0,144.0,140.0,NY +10/1/2020,-0.4665838505409578,1.3250981911519295,1600708.686,74793.41323,179277.9689,613308.2247,254071.3821,29663.0,29663.0,222.0,222.0,50224.0,401.0,149054.0,140175.0,5385.0,5342.0,179.0,170.0,NY +11/1/2020,-1.648553189199655,1.20019908415716,1607511.746,74904.78301,179219.6222,609990.8059,254124.4052,91730.0,91730.0,739.0,739.0,144652.0,895.0,343145.0,311979.0,9385.0,9229.0,895.0,872.0,NY +12/1/2020,-5.124731521187366,4.614450824769324,1607604.904,74704.41373,178416.8524,604153.6335,253121.2662,212153.0,212153.0,2564.0,2564.0,322370.0,3345.0,610029.0,554816.0,21133.0,20852.0,2512.0,2408.0,NY +1/1/2021,-9.250653381781074,-3.7538153499725215,1609725.891,75656.25657,179051.9365,606527.1992,254708.1931,263125.0,263125.0,3654.0,3654.0,441124.0,5651.0,682529.0,609083.0,35074.0,34683.0,4633.0,4443.0,NY +2/1/2021,-6.551075560476649,-4.081174498580027,1612615.416,76638.10224,179769.6403,609179.0382,256407.7425,107667.0,107667.0,1783.0,1783.0,223960.0,3967.0,401042.0,349294.0,25950.0,25664.0,2539.0,2449.0,NY +3/1/2021,-6.388080750697957,-1.891046734280461,1611320.139,77413.27267,180017.1827,610235.629,257430.4553,107227.0,107227.0,781.0,781.0,229271.0,2680.0,446261.0,384801.0,25398.0,25133.0,2095.0,2028.0,NY +4/1/2021,-3.711496227063821,-2.3059996094110247,1618945.155,78223.82511,180364.3855,611627.6254,258588.2106,92985.0,92985.0,625.0,625.0,175012.0,1958.0,289569.0,255771.0,16354.0,16161.0,712.0,666.0,NY +5/1/2021,-0.7103028251562091,-2.4289651142695403,1626843.844,79043.03004,180747.2703,613138.4033,259790.3004,32062.0,32062.0,371.0,371.0,54254.0,1043.0,84762.0,76559.0,6209.0,6152.0,55.0,55.0,NY +6/1/2021,0.3353254019848968,-1.9608346389740567,1633841.83,79813.87171,181035.3115,614325.2092,260849.1833,6255.0,6255.0,150.0,150.0,12582.0,372.0,25144.0,22066.0,2676.0,2647.0,0.0,0.0,NY +7/1/2021,-0.3692870563786512,-2.694730057547363,1638460.102,80653.34035,181493.7076,616087.9905,262147.0479,15309.0,15309.0,55.0,55.0,35404.0,171.0,94495.0,80464.0,5059.0,4984.0,48.0,45.0,NY +8/1/2021,-2.4852205220871277,-3.1302692288731677,1644024.223,81534.18524,182057.3266,618206.205,263591.5118,70452.0,70452.0,305.0,305.0,128200.0,613.0,262266.0,238176.0,12209.0,12071.0,302.0,290.0,NY +9/1/2021,-1.6249644798076566,-3.7296590661707176,1650872.201,82474.44524,182763.4879,620807.0081,265237.9332,97576.0,97576.0,678.0,678.0,148093.0,1104.0,277724.0,254648.0,9305.0,9153.0,269.0,256.0,NY +10/1/2021,-0.8316197116193367,-1.5205022491401723,1661674.994,83190.89653,182985.8244,621762.5845,266176.7209,97641.0,97641.0,792.0,792.0,132900.0,1081.0,241388.0,225684.0,6621.0,6534.0,219.0,205.0,NY +11/1/2021,-1.418751415622475,-1.130500142201564,1671647.659,83863.71968,183128.1373,622443.8945,266991.857,131053.0,131053.0,783.0,783.0,170012.0,1095.0,364561.0,343130.0,8608.0,8465.0,490.0,479.0,NY +12/1/2021,-9.984708977847053,-1.6443438355563726,1682631.331,84585.28622,183392.0953,623536.4058,267977.3815,355100.0,355100.0,1390.0,1390.0,750685.0,1974.0,2002692.0,1865917.0,37574.0,36912.0,2619.0,2572.0,NY +1/1/2022,-12.306497302554904,0.4167982700266464,1682520.04,84532.31243,183756.9509,625026.6736,268289.2633,629777.0,629777.0,2316.0,2316.0,1325136.0,5338.0,2146111.0,2039264.0,45406.0,44844.0,5303.0,4988.0,NY +2/1/2022,-0.2369262643155343,0.9804469110053902,1681275.968,84422.95318,183993.8952,626079.0938,268416.8483,76202.0,76202.0,1030.0,1030.0,119805.0,2512.0,190529.0,184384.0,4610.0,4561.0,117.0,117.0,NY +3/1/2022,0.494141210616757,3.923405853680776,1674036.482,84013.25822,183569.0235,624875.7841,267582.2817,40627.0,40627.0,277.0,277.0,70513.0,609.0,137617.0,128413.0,2140.0,2107.0,0.0,0.0,NY +4/1/2022,-0.0702637820617551,-2.172379347806485,1670064.685,84230.92004,184509.5684,628317.635,268740.4885,108578.0,108578.0,251.0,251.0,172388.0,377.0,324014.0,301879.0,4047.0,3999.0,87.0,87.0,NY +5/1/2022,-1.0741564757160056,1.1827521547478304,1659365.222,84105.92123,184695.7959,629188.8662,268801.7171,151342.0,151342.0,433.0,433.0,269689.0,645.0,549008.0,504975.0,7441.0,7341.0,173.0,171.0,NY +6/1/2022,-1.222576407350947,4.344289313258767,1642453.041,83659.442,184168.7852,627626.6274,267828.2272,71061.0,71061.0,321.0,321.0,173282.0,678.0,335813.0,299986.0,7940.0,7870.0,112.0,109.0,NY +7/1/2022,-2.250135265125272,-4.474910267539592,1652247.84,84116.59792,185626.9939,632827.716,269743.5918,86951.0,86951.0,265.0,265.0,206851.0,602.0,430568.0,379685.0,11418.0,11243.0,271.0,256.0,NY +8/1/2022,-1.149630862664142,-1.4824245061543038,1656069.712,84269.74223,186413.5308,635738.5825,270683.273,73805.0,73805.0,321.0,321.0,170440.0,855.0,284309.0,254339.0,7696.0,7595.0,52.0,48.0,NY +9/1/2022,0.0431641982999023,-0.8295339885771285,1658601.092,84357.47137,187052.6322,638145.247,271410.1036,71875.0,71875.0,297.0,297.0,132929.0,704.0,136978.0,124331.0,3664.0,3616.0,25.0,25.0,NY +10/1/2022,-0.3093911451060517,-0.5255808579922923,1656528.249,84415.46371,187622.6031,640314.4417,272038.0668,16583.0,16583.0,85.0,85.0,0.0,0.0,136088.0,122233.0,4856.0,4791.0,170.0,164.0,NY +11/1/2022,-0.6668294684611846,-2.791349438959642,1659086.449,84708.6767,188713.2915,644259.6705,273421.9682,0.0,0.0,0.0,0.0,0.0,,149083.0,130880.0,6065.0,5974.0,257.0,239.0,NY +12/1/2022,-1.1122574301294392,-3.129714675806853,1662385.245,85039.4603,189887.0274,648488.0898,274926.4877,0.0,0.0,0.0,0.0,0.0,,201644.0,176701.0,7571.0,7456.0,418.0,390.0,NY +2/1/2012,1.416643043338221,-0.6009173389565067,418891.6905,30497.56392,73596.24625,219612.6375,104093.8102,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +3/1/2012,1.4134858838492017,-0.4241981123360832,418724.6202,30553.71735,73549.38086,219898.3306,104103.0982,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +4/1/2012,1.4102613700630382,-0.0622489575615907,419578.3683,30627.74117,73546.11847,220313.0194,104173.8596,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +5/1/2012,1.4069878396024285,1.558085277670548,421454.0436,30776.35985,73722.01449,221264.3102,104498.3743,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +6/1/2012,1.403683263472844,1.0937465277570877,423070.6892,30906.082,73852.35296,222079.5555,104758.435,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +7/1/2012,1.400364952777614,0.4663571197944707,424063.1976,31009.17385,73919.03115,222703.4289,104928.205,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +8/1/2012,1.3970493078800827,-1.613348171355196,423785.0279,31019.23557,73764.60988,222659.6053,104783.8454,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +9/1/2012,1.3937516145666295,-1.429550575669682,423639.5063,31038.8129,73633.95232,222684.8245,104672.7652,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +10/1/2012,1.3904858889980063,-1.415832375362189,422664.0643,31060.12297,73508.46436,222723.1642,104568.5873,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +11/1/2012,1.3872647707626151,1.037825413777787,423228.4388,31194.37625,73650.67572,223571.6487,104845.052,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +12/1/2012,1.384099461293752,0.654107550685353,423574.5032,31312.61619,73754.93325,224305.2698,105067.5494,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +1/1/2013,1.38099970335356,-0.6213957191819739,425276.1765,31357.45847,73740.71888,224487.8638,105098.1773,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +2/1/2013,1.3779737962339302,-1.8300337836100096,426232.6506,31347.80273,73599.01209,224281.0774,104946.8148,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +3/1/2013,1.3750286407497256,1.5365288298410578,429304.9108,31493.94197,73823.36201,225189.2638,105317.304,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +4/1/2013,1.3721698079414626,1.1454787555409756,429856.8183,31623.54044,74008.69129,225978.8926,105632.2317,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +5/1/2013,1.369401625584068,0.1392581842416318,429791.7368,31707.75996,74087.80484,226444.2299,105795.5648,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +6/1/2013,1.3667272770297871,-0.9110519287227872,429081.4794,31744.13206,74055.44909,226568.242,105799.5811,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +7/1/2013,1.3641489075136293,-0.3115719964938911,429973.5251,31809.19516,74090.42399,226897.4913,105899.6192,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +8/1/2013,1.3616677337449166,-0.5347931582671883,430736.0194,31864.67671,74103.40525,227158.7693,105968.082,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +9/1/2013,1.3592841533365978,0.2249672779828464,431989.0694,31956.45002,74201.00935,227679.0251,106157.4594,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +10/1/2013,1.3569978513377747,-0.6824183971092299,432464.7241,32006.56364,74202.12278,227902.748,106208.6864,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +11/1/2013,1.35480790179996,-0.6131059372929821,432994.0159,32060.59848,74212.70077,228154.8244,106273.2992,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +12/1/2013,1.352712862902652,-0.9248565532260666,433334.8027,32100.62302,74191.25426,228307.6682,106291.8773,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +1/1/2014,1.3507108646770574,0.0047965591783354,431865.1779,32215.38356,74242.31832,228631.7683,106457.7019,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +2/1/2014,1.348799688794904,0.8782932526875313,430964.6791,32372.04167,74390.46754,229254.4314,106762.5092,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +3/1/2014,1.346976840234615,-0.1098193623888077,429457.1058,32482.51953,74433.04272,229551.3102,106915.5623,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +4/1/2014,1.345239610905721,-0.0883750065797628,431772.3359,32594.45991,74479.79729,229860.4271,107074.2572,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +5/1/2014,1.3435851355131074,0.0396658079143002,434171.4034,32712.95873,74542.30187,230217.5497,107255.2606,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI +6/1/2014,1.34201044008476,0.4260147559055669,436820.692,32850.4877,74648.76013,230709.9529,107499.2478,0.0,0.0,0.0,0.0,0.0,,,,,,,,MI 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+4/1/2020,-14.919262350785573,3.482742074850857,472994.3969,39996.28074,80900.75625,260636.6284,120897.037,34532.0,32213.0,3944.0,3736.0,35527.0,3699.0,33141.0,31257.0,7828.0,7754.0,3664.0,3538.0,MI +5/1/2020,-0.1232879536170088,-0.8656317153700286,453741.7802,40142.85973,80842.37251,259446.0277,120985.2322,43686.0,38520.0,1430.0,1381.0,19233.0,1740.0,19087.0,18410.0,1852.0,1834.0,344.0,334.0,MI +6/1/2020,1.2084070591427496,-2.5777427655340714,433442.7429,40188.35895,80582.05262,257611.9679,120770.4116,27082.0,23586.0,377.0,371.0,8017.0,462.0,13573.0,13143.0,784.0,778.0,44.0,44.0,MI +7/1/2020,1.3226721461219864,-2.159803145413034,452067.6978,40257.80575,80372.02595,255945.1752,120629.8317,41363.0,37475.0,236.0,227.0,19846.0,257.0,28012.0,26737.0,1471.0,1464.0,0.0,0.0,MI +8/1/2020,0.7930962892885027,-2.2360341659535887,470528.7329,40322.14054,80153.87469,254258.4664,120476.0152,39178.0,35519.0,290.0,287.0,22451.0,303.0,28962.0,27642.0,1436.0,1432.0,106.0,106.0,MI +9/1/2020,0.8234982010465188,-1.394315157555987,489477.8386,40435.65714,80035.16615,252892.0242,120470.8233,36796.0,33019.0,290.0,279.0,24989.0,330.0,37712.0,36798.0,1733.0,1726.0,84.0,84.0,MI +10/1/2020,-1.9506039997389344,-0.3736719598646411,491160.5522,40609.41531,80036.424,251906.7516,120645.8393,66505.0,60905.0,672.0,635.0,59392.0,616.0,87812.0,87697.0,4413.0,4409.0,680.0,658.0,MI +11/1/2020,-13.904767296898228,-0.2721656138532036,492917.2413,40789.27337,80049.86597,250960.2968,120839.1393,173167.0,163156.0,2377.0,2275.0,191536.0,1865.0,200689.0,200442.0,9116.0,9102.0,3334.0,3323.0,MI +12/1/2020,-12.19322113156528,-3.1183533788718467,492617.9224,40798.84084,79730.62862,248975.3009,120529.4695,107516.0,97938.0,3642.0,3333.0,139679.0,3454.0,188965.0,188576.0,7742.0,7730.0,2969.0,2911.0,MI +1/1/2021,-4.506024071392517,5.358112782478671,491684.2093,41332.72785,80389.35719,249878.8745,121722.085,63934.0,55097.0,2008.0,1840.0,81517.0,2507.0,161783.0,156040.0,5167.0,5057.0,1257.0,1193.0,MI +2/1/2021,-0.1343176506596428,5.573249679260661,490996.5976,41882.90649,81082.4975,250897.6254,122965.404,34918.0,28652.0,820.0,777.0,35412.0,983.0,66722.0,62439.0,2507.0,2456.0,271.0,259.0,MI +3/1/2021,-1.2816854832589328,3.69360484982819,489049.2995,42320.01109,81560.16791,251260.0324,123880.179,110358.0,93881.0,661.0,609.0,100801.0,611.0,168397.0,158635.0,5490.0,5398.0,486.0,459.0,MI +4/1/2021,-7.941356220374622,3.9853707243777614,492190.7188,42776.30911,82078.90544,251761.352,124855.2146,157899.0,138908.0,1862.0,1743.0,190116.0,1634.0,248877.0,237239.0,7948.0,7786.0,1954.0,1876.0,MI +5/1/2021,-2.334264532509316,4.035869155884003,495406.8287,43237.20377,82610.15163,252312.306,125847.3554,39926.0,33951.0,1440.0,1377.0,55474.0,1623.0,105560.0,101109.0,3783.0,3721.0,696.0,676.0,MI +6/1/2021,0.6992460694150893,3.595805413499276,498340.089,43671.50825,83094.28537,252730.6899,126765.7936,6259.0,5211.0,438.0,403.0,8078.0,623.0,61389.0,60774.0,1117.0,1106.0,24.0,24.0,MI +7/1/2021,0.7868336491328911,4.1586112358854495,497752.4874,44143.23711,83653.0702,253386.7306,127796.3073,13080.0,11019.0,160.0,149.0,11087.0,189.0,65794.0,64095.0,1217.0,1204.0,0.0,0.0,MI +8/1/2021,-0.1292223272582167,4.474522323585534,497469.0839,44637.50816,84257.41129,254189.5389,128894.9194,46562.0,40150.0,231.0,208.0,49237.0,359.0,84137.0,78444.0,3162.0,3075.0,182.0,178.0,MI +9/1/2021,-1.3504359078378982,4.928454961913736,497588.0077,45164.22248,84925.30477,255191.254,130089.5273,91984.0,77824.0,392.0,328.0,89721.0,808.0,123861.0,114184.0,3471.0,3401.0,441.0,416.0,MI +10/1/2021,-4.438012897203919,3.0445513918856726,498285.1403,45568.27178,85365.38445,255517.2698,130933.6562,120455.0,101990.0,694.0,570.0,125039.0,1307.0,169656.0,156590.0,4791.0,4691.0,1127.0,1064.0,MI +11/1/2021,-12.27223347063392,2.6771372415312857,498763.2826,45948.2868,85764.1709,255731.3593,131712.4577,209153.0,180423.0,1057.0,860.0,199950.0,1737.0,220260.0,201217.0,7139.0,6958.0,2935.0,2730.0,MI +12/1/2021,-14.60981519337108,3.0638565709398144,499571.6966,46354.87753,86216.1207,256115.1454,132570.9982,226015.0,198475.0,1649.0,1295.0,235272.0,3621.0,261195.0,243230.0,6832.0,6710.0,3492.0,3270.0,MI +1/1/2022,-11.70043190074432,-0.837934076371873,500505.989,46334.30011,86340.00592,256381.6346,132674.306,548194.0,473280.0,1535.0,1146.0,548140.0,3243.0,408383.0,371146.0,5614.0,5491.0,2825.0,2596.0,MI +2/1/2022,-2.8106909731123157,-1.3042142841426785,501092.5461,46282.7223,86404.3197,256472.3577,132687.042,101934.0,76138.0,2242.0,1974.0,101934.0,2242.0,451269.0,438307.0,2281.0,2235.0,796.0,732.0,MI +3/1/2022,0.2991813034393118,-3.752292316250799,499877.5974,46066.36526,86158.5587,255644.2207,132224.924,27303.0,22201.0,1186.0,1032.0,27122.0,1157.0,262121.0,255069.0,985.0,971.0,86.0,79.0,MI +4/1/2022,0.5485108713853855,1.3278274002841162,502395.7674,46193.90769,86554.18727,256720.3014,132748.095,37419.0,31598.0,311.0,277.0,38425.0,340.0,210766.0,186441.0,1164.0,1135.0,26.0,20.0,MI +5/1/2022,-0.316506703234392,-1.4633975613550607,502860.3707,46133.4628,86596.32734,256748.7122,132729.7901,101885.0,85949.0,355.0,302.0,101885.0,355.0,172831.0,145524.0,1939.0,1882.0,206.0,185.0,MI +6/1/2022,-0.0433066085998632,-4.093644664545355,501383.7522,45896.55256,86304.77201,255789.2755,132201.3246,78600.0,66093.0,561.0,441.0,78600.0,561.0,106409.0,91574.0,1438.0,1406.0,137.0,114.0,MI +7/1/2022,-0.0372289111138499,3.2544562007308717,504140.3492,46155.31577,86943.92219,257589.1027,133099.238,65881.0,54890.0,510.0,381.0,65881.0,510.0,117164.0,99291.0,1597.0,1541.0,122.0,110.0,MI +8/1/2022,-0.4856922077714216,0.7652281377115839,505074.8685,46247.25204,87268.55145,258457.2611,133515.8035,98047.0,81248.0,610.0,500.0,98047.0,610.0,119255.0,99085.0,1702.0,1659.0,216.0,174.0,MI +9/1/2022,-0.6194961873871057,0.2243804008874797,505617.2173,46303.24033,87524.42531,259122.3601,133827.6656,65808.0,51457.0,586.0,470.0,65808.0,586.0,86801.0,68452.0,1211.0,1172.0,246.0,205.0,MI +10/1/2022,-0.6371873815324207,-0.0258890248559926,506131.6129,46342.85004,87748.26209,259693.2749,134091.1121,12880.0,10118.0,143.0,120.0,0.0,0.0,69797.0,54914.0,1217.0,1177.0,269.0,214.0,MI +11/1/2022,-0.4834588208650601,1.8640489795844972,508055.5377,46511.55567,88215.83269,260985.9592,134727.3884,0.0,0.0,0.0,0.0,0.0,,296807.0,185647.0,1097.0,1058.0,231.0,204.0,MI +12/1/2022,-0.3446498514845295,2.1487472571446418,510204.5526,46700.87894,88722.28766,262393.8178,135423.1666,0.0,0.0,0.0,0.0,0.0,,53413.0,41704.0,1197.0,1173.0,215.0,184.0,MI +2/1/2012,-1.1078044464760908,1.083050515528359,53664.70775,4697.85859,11807.20612,32818.87589,16505.06471,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +3/1/2012,-1.1262928005063486,0.9334968362947688,53595.52815,4690.905901,11788.96576,32768.46142,16479.87166,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +4/1/2012,-1.1358927293328165,0.6028660511448597,53758.23604,4686.746908,11777.74885,32737.56834,16464.49576,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +5/1/2012,-1.140869390680082,-0.9514316359224586,54051.86759,4694.005587,11795.22459,32786.42962,16489.23017,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +6/1/2012,-1.1434487823155652,-0.4811887515376961,54312.3646,4698.357137,11805.39397,32814.98235,16503.7511,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +7/1/2012,-1.1447857552463478,0.1467435977508016,54206.3699,4698.658178,11805.38567,32815.24464,16504.04385,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +8/1/2012,-1.1454787998574232,2.184907435954897,53938.81256,4684.919644,11770.10575,32717.4619,16455.0254,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +9/1/2012,-1.1458380709731717,2.02315667427897,53689.61635,4672.715409,11738.68538,32630.40576,16411.40079,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +10/1/2012,-1.1460243210761607,2.0258978029628345,53308.03792,4660.862627,11708.15247,32545.81483,16369.0151,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +11/1/2012,-1.1461208767765163,-0.3427082068952369,53121.2582,4665.992439,11720.28182,32579.81393,16386.27426,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +12/1/2012,-1.146170933583004,0.0445536511018139,52906.86231,4668.714523,11726.36267,32596.99979,16395.07719,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +1/1/2013,-1.1461968843602692,0.1444034493925672,53169.32409,4676.074295,11726.39659,32593.02622,16402.47089,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +2/1/2013,-1.1462103379645934,1.3348597310460415,53338.3421,4675.303492,11706.14365,32532.68878,16381.44714,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +3/1/2013,-1.1462173126958335,-1.9202516002368213,53772.01231,4697.766845,11744.10778,32634.15262,16441.87462,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +4/1/2013,-1.1462209285973035,-1.5253124421863686,53684.65024,4717.764305,11775.86913,32718.3719,16493.63343,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +5/1/2013,-1.14622280318536,-0.5333898248902842,53520.21252,4730.991981,11790.73029,32755.63476,16521.72227,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +6/1/2013,-1.1462237750262751,0.500806248688038,53275.87229,4737.078674,11787.84526,32743.60849,16524.92393,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +7/1/2013,-1.1462242788568888,-0.0683974678024366,53378.35705,4747.4446,11795.66996,32761.34473,16543.11456,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +8/1/2013,-1.1462245400573547,0.1611576119754394,53464.78179,4756.378682,11799.98687,32769.34949,16556.36555,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +9/1/2013,-1.146224675471288,-0.5647538096025014,53612.11226,4770.728705,11817.7745,32814.77105,16588.5032,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +10/1/2013,-1.1462247456738175,0.3284415319806582,53764.6068,4778.858064,11820.18967,32817.51571,16599.04773,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +11/1/2013,-1.1462247820688558,0.2723350720835634,53923.46627,4787.570793,11824.10525,32824.43897,16611.67604,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +12/1/2013,-1.146224800937104,0.5859762944830029,54058.52043,4794.189099,11822.91054,32817.18977,16617.09964,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +1/1/2014,-1.1462248107189543,-0.1473464442296737,53964.6519,4806.361094,11835.17798,32775.0836,16641.53907,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +2/1/2014,-1.1462248157901511,-0.9868046263907072,53941.67693,4824.796263,11862.91179,32775.99719,16687.70805,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +3/1/2014,-1.146224818419206,-0.0181453694671633,53842.48559,4836.361331,11873.79928,32730.56181,16710.16062,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +4/1/2014,-1.1462248197821856,-0.030703870351369,54017.21464,4848.163361,11885.33738,32687.21652,16733.50074,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +5/1/2014,-1.1462248204887944,-0.1471273520881575,54202.85208,4860.958618,11899.37394,32651.01682,16760.33256,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +6/1/2014,-1.1462248208551205,-0.5148297604906009,54420.05144,4876.595332,11920.41539,32634.2505,16797.01072,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +7/1/2014,-1.1462248210450343,-0.238809964097113,54567.07691,4890.347902,11936.89634,32605.1943,16827.24424,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +8/1/2014,-1.1462248211434916,-1.2945625761431805,54802.74997,4912.044351,11972.79109,32629.26815,16884.83544,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +9/1/2014,-1.146224821194535,-1.0193713777234472,55017.68359,4931.881825,12004.15686,32641.0085,16936.03868,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +10/1/2014,-1.1462248212209971,-1.3301190921500925,55290.00326,4954.20491,12041.57257,32669.19953,16995.77748,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +11/1/2014,-1.1462248212347157,-1.648288337647851,55591.02343,4979.097558,12085.21665,32714.21366,17064.31421,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +12/1/2014,-1.146224821241828,-2.135497086834474,55935.39926,5007.868661,12138.23587,32784.43754,17146.10453,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +1/1/2015,-1.146224821245515,-1.994482324912049,56020.13696,5050.862896,12174.11636,33063.33057,17224.97926,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +2/1/2015,-1.1462248212474266,2.181147084878696,55753.61641,5062.292548,12133.71462,33135.59184,17196.00717,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +3/1/2015,-1.1462248212484178,2.372644726223787,55472.86324,5072.303099,12090.15277,33198.57339,17162.45587,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +4/1/2015,-1.1462248212489312,1.6218404638439516,55729.43996,5088.102873,12060.57073,33299.4526,17148.6736,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +5/1/2015,-1.146224821249198,2.7079506373526403,55894.98753,5095.615702,12011.55693,33346.10525,17107.17263,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +6/1/2015,-1.1462248212493358,2.419867871939457,56084.69755,5105.363985,11968.10055,33407.39773,17073.46453,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +7/1/2015,-1.1462248212494075,1.690693459363505,56182.07481,5120.761271,11938.06937,33505.66184,17058.83064,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +8/1/2015,-1.1462248212494446,1.1618137444439918,56324.99372,5140.304067,11917.75746,33631.05251,17058.06152,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +9/1/2015,-1.146224821249464,0.1830932156496021,56552.10139,5167.539622,11915.18458,33806.77032,17082.7242,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +10/1/2015,-1.146224821249474,1.078185352369309,56783.31429,5187.914351,11896.68584,33937.59965,17084.60019,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +11/1/2015,-1.146224821249479,1.5880314926818533,56971.55907,5204.363716,11869.21593,34042.75171,17073.57965,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +12/1/2015,-1.1462248212494814,0.6523299729683735,57240.95184,5228.225814,11858.63849,34196.39139,17086.8643,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +1/1/2016,-1.1462248212494828,0.0566721233502338,57255.75797,5245.056757,11870.66418,34345.38329,17115.72093,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME +2/1/2016,-1.1462248212494837,-0.6838073641360358,57335.45133,5267.833504,11896.14369,34533.31332,17163.97719,0.0,0.0,0.0,0.0,0.0,,,,,,,,ME 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CliRunner() + # TODO: Change to be test.h5ad with small state subset def test_run_dfm(tmpdir): result = runner.invoke(app, ["run", str(DATA_DIR / "test.h5ad"), str(tmpdir), "--batch", "State"]) diff --git a/tests/test_covid19.py b/tests/test_covid19.py index 7cf2f4e..6374bab 100644 --- a/tests/test_covid19.py +++ b/tests/test_covid19.py @@ -3,14 +3,14 @@ from dfmdash.covid19 import ( _get_raw_df, - get_raw, - get_df, - get_project_h5ad, - get_govt_fund_dist, + add_datetime, adjust_inflation, adjust_pandemic_response, - add_datetime, fix_names, + get_df, + get_govt_fund_dist, + get_project_h5ad, + get_raw, ) diff --git a/tests/test_dfm.py b/tests/test_dfm.py index 43c7c8d..4883a91 100644 --- a/tests/test_dfm.py +++ b/tests/test_dfm.py @@ -1,9 +1,11 @@ import shutil import unittest from pathlib import Path -from dfmdash.dfm import ModelRunner + from anndata import AnnData + from dfmdash.covid19 import get_project_h5ad +from dfmdash.dfm import ModelRunner COLUMNS = ["PCE", "CPIU", "Hosp1", "Hosp2"] STATES = ["AK", "CA"] diff --git a/tests/test_io.py b/tests/test_io.py index 7522a1b..a3c0db8 100644 --- a/tests/test_io.py +++ b/tests/test_io.py @@ -1,18 +1,20 @@ import shutil -import pandas as pd -import numpy as np from pathlib import Path + +import numpy as np +import pandas as pd +import pytest from anndata import AnnData -from dfmdash.io import DataLoader + from dfmdash.covid19 import DATA_DIR -import pytest +from dfmdash.io import DataLoader @pytest.fixture() def dfs(): data = pd.read_csv(DATA_DIR / "data.csv") - factors = pd.read_csv(DATA_DIR / "factors.csv", index_col=0) - metadata = pd.read_csv(DATA_DIR / "metadata.csv", index_col=0) + factors = pd.read_csv(DATA_DIR / "../factors.csv", index_col=0) + metadata = pd.read_csv(DATA_DIR / "../metadata.csv", index_col=0) return data, factors, metadata diff --git a/tests/test_processing.py b/tests/test_processing.py index 2b9d2ae..204b886 100644 --- a/tests/test_processing.py +++ b/tests/test_processing.py @@ -1,8 +1,8 @@ from functools import reduce +from pathlib import Path import pandas as pd import pytest -from pathlib import Path from dfmdash.covid19 import get_project_h5ad from dfmdash.processing import DataProcessor, is_constant diff --git a/tests/test_results.py b/tests/test_results.py index a03b7cd..4e7a96e 100644 --- a/tests/test_results.py +++ b/tests/test_results.py @@ -7,7 +7,7 @@ @pytest.fixture def runs_dir() -> pd.DataFrame: - return ROOT_DIR / "data" / "example-data" + return ROOT_DIR / "../data" / "example-data" @pytest.fixture