reorganize and rename

code/do_all.py

@@ -46,7 +46,7 @@
 still run.
 """
 
-from estimark.calibration.options import (
+from estimark.options import (
     all_replications,
     high_resource,
     low_resource,
@@ -137,11 +137,11 @@ def run_replication():
         return
 
     if subjective_markets == "2" or subjective_markets == "4":
-        replication_specs["subjective_stock_market"] = True
+        replication_specs["subjective_stock"] = True
         print("Adding subjective stock market beliefs...")
 
     if subjective_markets == "3" or subjective_markets == "4":
-        replication_specs["subjective_labor_market"] = True
+        replication_specs["subjective_labor"] = True
         print("Adding subjective labor market beliefs...")
 
     estimate(**replication_specs)

code/estimark/calibration/estimation_parameters.py → code/estimark/calibration/parameters.py

@@ -3,27 +3,12 @@
 model.  The empirical data is stored in a separate csv file and is loaded in setup_scf_data.
 """
 
+from pathlib import Path
+
 import numpy as np
 from HARK.Calibration.Income.IncomeTools import CGM_income, parse_income_spec
-from HARK.datasets.life_tables.us_ssa.SSATools import parse_ssa_life_table
-from pathlib import Path
 from HARK.ConsumptionSaving.ConsIndShockModel import init_lifecycle
-
-# ---------------------------------------------------------------------------------
-# Debugging flags
-# ---------------------------------------------------------------------------------
-
-show_PermGroFacAgg_error = False
-# Error Notes:
-# This sets a "quick fix" to the error, AttributeError: 'TempConsumerType' object has no attribute 'PermGroFacAgg'
-# If you set this flag to "True" you will see the error. A more thorough fix is to
-# fix the place where this error was introduced (Set to "True" and it will appear;
-# this was almost certainly introduced when the code was extended to be used in the
-# GE setting). An even more thorough solution, which moves beyond the scope of
-# fixing this error, is adding unit tests to ID when changes to some code will
-# break things elsewhere.
-# Note: alternatively, decide that the "init_consumer_objects['PermGroFacAgg'] = 1.0"
-# line below fixes it properly ('feature not a bug') and remove all this text.
+from HARK.datasets.life_tables.us_ssa.SSATools import parse_ssa_life_table
 
 # ---------------------------------------------------------------------------------
 # - Define all of the model parameters for EstimatingMicroDSOPs and ConsumerExamples -
@@ -32,9 +17,7 @@
 exp_nest = 1  # Number of times to "exponentially nest" when constructing a_grid
 aXtraMin = 0.001  # Minimum end-of-period "assets above minimum" value
 aXtraMax = 100  # Maximum end-of-period "assets above minimum" value
-aXtraHuge = None  # A very large value of assets to add to the grid, not used
-aXtraExtra = None  # Some other value of assets to add to the grid, not used
-aXtraCount = 200  # Number of points in the grid of "assets above minimum"
+aXtraCount = 100  # Number of points in the grid of "assets above minimum"
 
 # Artificial borrowing constraint; imposed minimum level of end-of period assets
 BoroCnstArt = 0.0
@@ -55,65 +38,64 @@
 final_age = 90  # Age at which the problem ends (die with certainty)
 retirement_age = 65  # Age at which the consumer retires
 initial_age = 25  # Age at which the consumer enters the model
-TT = final_age - initial_age  # Total number of periods in the model
+terminal_t = final_age - initial_age  # Total number of periods in the model
 retirement_t = retirement_age - initial_age - 1
 
 # Initial guess of the coefficient of relative risk aversion during estimation (rho)
-CRRA_start = 5.0
+init_CRRA = 5.0
 # Initial guess of the adjustment to the discount factor during estimation (beth)
-DiscFacAdj_start = 0.99
+init_DiscFacAdj = 0.99
 # Bounds for beth; if violated, objective function returns "penalty value"
-DiscFacAdj_bound = [0.0001, 15.0]
+bounds_DiscFacAdj = [0.0001, 15.0]
 # Bounds for rho; if violated, objective function returns "penalty value"
-CRRA_bound = [0.0001, 15.0]
+bounds_CRRA = [0.0001, 15.0]
 
 # Income
 ss_variances = True
 income_spec = CGM_income["HS"]
 # Replace retirement age
 income_spec["age_ret"] = retirement_age
 inc_calib = parse_income_spec(
-    age_min=initial_age, age_max=final_age, **income_spec, SabelhausSong=ss_variances
+    age_min=initial_age,
+    age_max=final_age,
+    **income_spec,
+    SabelhausSong=ss_variances,
 )
 
 # Age-varying discount factors over the lifecycle, lifted from Cagetti (2003)
-
-
 # Get the directory containing the current file and construct the full path to the CSV file
 csv_file_path = Path(__file__).resolve().parent / ".." / "data" / "Cagetti2003.csv"
-DiscFac_timevary = np.genfromtxt(csv_file_path) * 0.0 + 1.0
+timevary_DiscFac = np.genfromtxt(csv_file_path) * 0.0 + 1.0  # todo
+constant_DiscFac = np.ones_like(timevary_DiscFac)
 
 # Survival probabilities over the lifecycle
 liv_prb = parse_ssa_life_table(
-    female=False, min_age=initial_age, max_age=final_age - 1, cohort=1960
+    female=False,
+    min_age=initial_age,
+    max_age=final_age - 1,
+    cohort=1960,
 )
 
-# Age groups for the estimation: calculate average wealth-to-permanent income ratio
-# for consumers within each of these age groups, compare actual to simulated data
-empirical_cohort_age_groups = [
-    [age for age in range(start, start + 5)] for start in range(26, 61, 5)
-]
-
 
 # Three point discrete distribution of initial w
-initial_wealth_income_ratio_vals = np.array([0.17, 0.5, 0.83])
+init_w_to_y = np.array([0.17, 0.5, 0.83])
 # Equiprobable discrete distribution of initial w
-initial_wealth_income_ratio_probs = np.array([0.33333, 0.33333, 0.33334])
+prob_w_to_y = np.array([0.33333, 0.33333, 0.33334])
 num_agents = 10000  # Number of agents to simulate
 bootstrap_size = 50  # Number of re-estimations to do during bootstrap
 seed = 31382  # Just an integer to seed the estimation
 
 options = {
-    "initial_wealth_income_ratio_vals": initial_wealth_income_ratio_vals,
-    "initial_wealth_income_ratio_probs": initial_wealth_income_ratio_probs,
+    "init_w_to_y": init_w_to_y,
+    "prob_w_to_y": prob_w_to_y,
     "num_agents": num_agents,
     "bootstrap_size": bootstrap_size,
     "seed": seed,
-    "DiscFacAdj_start": DiscFacAdj_start,
-    "CRRA_start": CRRA_start,
-    "DiscFacAdj_bound": DiscFacAdj_bound,
-    "CRRA_bound": CRRA_bound,
-    "DiscFac_timevary": DiscFac_timevary,
+    "init_DiscFacAdj": init_DiscFacAdj,
+    "init_CRRA": init_CRRA,
+    "bounds_DiscFacAdj": bounds_DiscFacAdj,
+    "bounds_CRRA": bounds_CRRA,
+    "timevary_DiscFac": timevary_DiscFac,
 }
 
 # -----------------------------------------------------------------------------
@@ -124,28 +106,28 @@
 init_consumer_objects = {
     **init_lifecycle,
     **{
-        "CRRA": CRRA_start,
+        "CRRA": init_CRRA,
         "Rfree": Rfree,
         "PermGroFac": inc_calib["PermGroFac"],
+        "PermGroFacAgg": 1.0,
         "BoroCnstArt": BoroCnstArt,
         "PermShkStd": inc_calib["PermShkStd"],
         "PermShkCount": PermShkCount,
         "TranShkStd": inc_calib["TranShkStd"],
         "TranShkCount": TranShkCount,
-        "T_cycle": TT,
+        "T_cycle": terminal_t,
         "UnempPrb": UnempPrb,
         "UnempPrbRet": UnempPrbRet,
         "T_retire": retirement_t,
-        "T_age": TT,
+        "T_age": terminal_t,
         "IncUnemp": IncUnemp,
         "IncUnempRet": IncUnempRet,
         "aXtraMin": aXtraMin,
         "aXtraMax": aXtraMax,
         "aXtraCount": aXtraCount,
-        "aXtraExtra": [aXtraExtra, aXtraHuge],
         "aXtraNestFac": exp_nest,
         "LivPrb": liv_prb,
-        "DiscFac": DiscFac_timevary,
+        "DiscFac": timevary_DiscFac,
         "AgentCount": num_agents,
         "seed": seed,
         "tax_rate": 0.0,
@@ -158,24 +140,22 @@
 ElnR = 0.020
 VlnR = 0.424**2
 
-init_subjective_stock_market = {
+TrueElnR = 0.085
+TrueVlnR = 0.170**2
+
+init_subjective_stock = {
     "Rfree": 1.019,  # from Mateo's JMP
     "RiskyAvg": np.exp(ElnR + 0.5 * VlnR),
     "RiskyStd": np.sqrt(np.exp(2 * ElnR + VlnR) * (np.exp(VlnR) - 1)),
+    "RiskyAvgTrue": np.exp(TrueElnR + 0.5 * TrueVlnR),
+    "RiskyStdTrue": np.sqrt(np.exp(2 * TrueElnR + TrueVlnR) * (np.exp(TrueVlnR) - 1)),
 }
 
-init_subjective_labor_market = {  # from Tao's JMP
+init_subjective_labor = {  # from Tao's JMP
     "TranShkStd": [0.03] * len(inc_calib["TranShkStd"]),
     "PermShkStd": [0.03] * len(inc_calib["PermShkStd"]),
 }
 
-if show_PermGroFacAgg_error:
-    pass  # do nothing
-else:
-    # print(
-    #     "***NOTE: using a 'quick fix' for an attribute error. See 'Error Notes' in EstimationParameter.py for further discussion.***"
-    # )
-    init_consumer_objects["PermGroFacAgg"] = 1.0
 
 if __name__ == "__main__":
     print("Sorry, estimation_parameters doesn't actually do anything on its own.")

code/estimark/calibration/setup_scf_data.py → code/estimark/calibration/scf.py

@@ -2,54 +2,53 @@
 Sets up the SCF data for use in the EstimatingMicroDSOPs estimation.
 """
 
-from estimark.calibration.estimation_parameters import (
-    empirical_cohort_age_groups,
-    initial_age,
-)
+from pathlib import Path
 
 import numpy as np  # Numerical Python
 import pandas as pd
-
-
-from pathlib import Path
+from estimark.calibration.parameters import initial_age
 
 # Get the directory containing the current file and construct the full path to the CSV file
 csv_file_path = Path(__file__).resolve().parent / ".." / "data" / "SCFdata.csv"
 
 # Read the CSV file
-scf_data = pd.read_csv(csv_file_path)
+scf_full_data = pd.read_csv(csv_file_path)
 
 
 # Keep only observations with normal incomes > 0,
 # otherwise wealth/income is not well defined
-scf_data = scf_data[scf_data.norminc > 0.0]
+scf_full_data = scf_full_data[scf_full_data.norminc > 0.0]
+
+# Age groups for the estimation: calculate average wealth-to-permanent income ratio
+# for consumers within each of these age groups, compare actual to simulated data
+age_groups = [list(range(start, start + 5)) for start in range(26, 61, 5)]
 
 # Initialize empty lists for the data
-w_to_y_data = []  # Ratio of wealth to permanent income
-empirical_weights = []  # Weighting for this observation
-empirical_groups = []  # Which age group this observation belongs to (1-7)
+scf_data = []  # Ratio of wealth to permanent income
+scf_weights = []  # Weighting for this observation
+scf_groups = []  # Which age group this observation belongs to (1-7)
 
 # Only extract the data required from the SCF dataset
-search_ages = [ages[-1] for ages in empirical_cohort_age_groups]
+search_ages = [ages[-1] for ages in age_groups]
 for idx, age in enumerate(search_ages):
-    age_data = scf_data[scf_data.age_group.str.contains(f"{age}]")]
-    w_to_y_data.append(age_data.wealth_income_ratio.values)
-    empirical_weights.append(age_data.weight.values)
+    age_data = scf_full_data[scf_full_data.age_group.str.contains(f"{age}]")]
+    scf_data.append(age_data.wealth_income_ratio.values)
+    scf_weights.append(age_data.weight.values)
     # create a group id 1-7 for every observation
-    empirical_groups.append([idx + 1] * len(age_data))
+    scf_groups.append([idx + 1] * len(age_data))
 
 # Convert SCF data to numpy's array format for easier math
-w_to_y_data = np.concatenate(w_to_y_data)
-empirical_weights = np.concatenate(empirical_weights)
-empirical_groups = np.concatenate(empirical_groups)
+scf_data = np.concatenate(scf_data)
+scf_weights = np.concatenate(scf_weights)
+scf_groups = np.concatenate(scf_groups)
 
 # Generate a single array of SCF data, useful for resampling for bootstrap
-scf_data_array = np.array([w_to_y_data, empirical_groups, empirical_weights]).T
+scf_array = np.array([scf_data, scf_groups, scf_weights]).T
 
 # Generate a mapping between the real ages in the groups and the indices of simulated data
-simulation_map_cohorts_to_age_indices = []
-for ages in empirical_cohort_age_groups:
-    simulation_map_cohorts_to_age_indices.append(np.array(ages) - initial_age)
+scf_mapping = []
+for ages in age_groups:
+    scf_mapping.append(np.array(ages) - initial_age)
 
 if __name__ == "__main__":
     print("Sorry, setup_scf_data doesn't actually do anything on its own.")