merino: Mastering Econometrics Regressions, Inference, and Numerical Optimization

Introduction

Welcome to merino, a comprehensive collection of Jupyter notebooks designed to help you master econometrics. These notebooks cover various topics in regression analysis, inference, and numerical optimization, making them an excellent resource for students, researchers, and practitioners in the field of econometrics.

Source

These notebooks are based on the content from:

Using Python for Introductory Econometrics

Notebooks

Each notebook corresponds to a chapter from the source material. Click on the "Open in Colab" badge to view and interact with the notebook in Google Colab.

1. Ch2. The Simple Regression Model

2. Ch3. Multiple Regression Analysis: Estimation

3. Ch4. Multiple Regression Analysis: Inference

4. Ch5. MRA - OLS Asymptotics

5. Ch6. MRA - Further Issues

6. Ch7. MRA - Qualitative Regressors

7. Ch8. Heteroskedasticity

8. Ch9. Specification and Data Issues

9. Ch10. Basic Regression Analysis with Time Series Data

10. Ch11. Further Issues in Using OLS with Time Series Data

11. Ch12. Serial Correlation and Heteroskedasticity in Time Series Regressions

How to Use

1. Click on the "Open in Colab" badge next to the notebook you want to explore.
2. The notebook will open in Google Colab, where you can run the code and interact with the content.
3. To save your changes, make sure to save a copy to your Google Drive or download the notebook.

Contributing

If you find any issues or have suggestions for improvement, please feel free to open an issue or submit a pull request on the GitHub repository.

License

Please refer to the original source material at Using Python for Introductory Econometrics for licensing information.