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merino: Mastering Econometrics Regressions, Inference, and Numerical Optimization

JHU AAP License: MIT

merino is a powerful, open-source resource designed to help you conquer the world of econometrics using Python. This repository provides a comprehensive collection of interactive MyST Markdown and Jupyter notebooks that delve into regression analysis, statistical inference, numerical optimization, and other vital econometric techniques. Whether you're a student, researcher, or a seasoned practitioner, merino offers a hands-on learning experience to enhance your understanding and practical skills.

A key feature of merino is that these notebooks can be executed directly in your browser without any server setup or on platforms like Google Colab.

Why merino?

  • Comprehensive Coverage: merino mirrors the in-depth curriculum of "Using Python for Introductory Econometrics," covering a wide range of topics from basic regression models to advanced time series analysis.
  • Interactive Learning: Each notebook is meticulously crafted using MyST Markdown and Jupyter, allowing you to experiment with code, modify parameters, and visualize results directly within your browser or in Google Colab.
  • In-Browser Execution: You can run these notebooks directly in your browser—no server setup required!
  • Practical Applications: merino emphasizes the practical application of econometric principles, bridging the gap between theoretical concepts and real-world data analysis.
  • Beginner-Friendly & Advanced-Ready: The notebooks are structured progressively, making them accessible to beginners while still offering valuable insights for those with prior econometrics knowledge.
  • Open Source & Community-Driven: merino is open-source, encouraging collaboration and contributions from the econometrics community.
  • Multiple Execution Options: Run the notebooks directly in your browser or on platforms like Google Colab – the choice is yours!

Based on the Books:

These notebooks are based on the excellent companion book to Jeffrey M. Wooldridge's "Introductory Econometrics":

This companion book introduces the Python programming language with a focus on implementing standard econometric tools and methods. It is designed to be used alongside Wooldridge's textbook, providing a seamless transition from theory to practice.

It is highly recommended to use merino in conjunction with Wooldridge's "Introductory Econometrics" and "Using Python for Introductory Econometrics" for a deeper understanding of the underlying theory and the practical implementation.

Notebooks (Interactive Table of Contents)

Explore the notebooks directly in your browser or open them in Google Colab!

Chapter Description Execute
1. Ch2. The Simple Regression Model Introduction to simple linear regression, OLS estimation, and basic concepts. Open in Colab
2. Ch3. Multiple Regression Analysis: Estimation Expanding to multiple regression, understanding OLS in a matrix context. Open in Colab
3. Ch4. Multiple Regression Analysis: Inference Hypothesis testing, confidence intervals, and p-values in multiple regression. Open in Colab
4. Ch5. MRA - OLS Asymptotics Exploring the asymptotic properties of OLS estimators. Open in Colab
5. Ch6. MRA - Further Issues Addressing issues like multicollinearity, model specification, and functional form. Open in Colab
6. Ch7. MRA - Qualitative Regressors Incorporating qualitative (dummy) variables into regression models. Open in Colab
7. Ch8. Heteroskedasticity Understanding and addressing heteroskedasticity in regression. Open in Colab
8. Ch9. Specification and Data Issues Dealing with model misspecification, measurement error, and other data problems. Open in Colab
9. Ch10. Basic Regression Analysis with Time Series Data Introduction to time series regression, stationarity, and basic time series models. Open in Colab
10. Ch11. Further Issues in Using OLS with Time Series Data Advanced topics in time series regression, including forecasting and trend analysis. Open in Colab
11. Ch12. Serial Correlation and Heteroskedasticity in Time Series Regressions Detecting and correcting for serial correlation and heteroskedasticity in time series data. Open in Colab

You can navigate the notebooks using the three-stripe menu button in the upper-left corner on mobile devices or the table of contents panel on the left side of the browser window.

Getting Started

  1. Choose your Execution Method:

    • Run in your browser: Click the notebook links in the table above to run them directly in your browser.
    • Open in Google Colab: Click the "Open in Colab" badge for a cloud-based experience.

  2. Run & Experiment: Execute the code cells, modify parameters, and observe how the results change.

  3. Save Your Work (Colab): To save your modifications in Colab, go to File > Save a copy in Drive. This will create a copy of the notebook in your Google Drive.

Contributing

We welcome contributions! If you'd like to improve merino by:

  • Fixing bugs
  • Adding new notebooks
  • Improving existing content
  • Suggesting enhancements

Please follow these steps:

  1. Fork the repository on GitHub.
  2. Create a new branch for your changes.
  3. Make your changes and commit them with clear, concise messages.
  4. Submit a pull request to the main branch of the merino repository.

Review the Contribution Guidelines for more details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

"Using Python for Introductory Econometrics" and Wooldridge's "Introductory Econometrics" have their own licensing terms, which should be respected.

Acknowledgements

  • Florian Heiss and Daniel Brunner for creating the excellent "Using Python for Introductory Econometrics."
  • Jeffrey M. Wooldridge for his foundational textbook, "Introductory Econometrics."
  • The Executable Book Project for developing MyST and Thebe, making in-browser interactivity possible.
  • The Google Colab Team for providing such a fantastic platform for interactive computing.
  • The Python and Econometrics Communities for their invaluable contributions to open-source tools and knowledge sharing.
  • Johns Hopkins University Advanced Academic Programs for their support for continuing education and the Applied Economics Program.

Start your econometrics journey with merino today!