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:
merinomirrors 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:
merinoemphasizes 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:
merinois 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”:
- Using Python for Introductory Econometrics by Florian Heiss and Daniel Brunner
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. |  |
| 2. Ch3. Multiple Regression Analysis: Estimation | Expanding to multiple regression, understanding OLS in a matrix context. | |
| 3. Ch4. Multiple Regression Analysis: Inference | Hypothesis testing, confidence intervals, and p-values in multiple regression. | |
| 4. Ch5. MRA - OLS Asymptotics | Exploring the asymptotic properties of OLS estimators. | |
| 5. Ch6. MRA - Further Issues | Addressing issues like multicollinearity, model specification, and functional form. | |
| 6. Ch7. MRA - Qualitative Regressors | Incorporating qualitative (dummy) variables into regression models. | |
| 7. Ch8. Heteroskedasticity | Understanding and addressing heteroskedasticity in regression. | |
| 8. Ch9. Specification and Data Issues | Dealing with model misspecification, measurement error, and other data problems. | |
| 9. Ch10. Basic Regression Analysis with Time Series Data | Introduction to time series regression, stationarity, and basic time series models. | |
| 10. Ch11. Further Issues in Using OLS with Time Series Data | Advanced topics in time series regression, including forecasting and trend analysis. | |
| 11. Ch12. Serial Correlation and Heteroskedasticity in Time Series Regressions | Detecting and correcting for serial correlation and heteroskedasticity in time series data. | |
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¶
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.
Run & Experiment: Execute the code cells, modify parameters, and observe how the results change.
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:
- Fork the repository on GitHub.
- Create a new branch for your changes.
- Make your changes and commit them with clear, concise messages.
- Submit a pull request to the main branch of the
merinorepository.
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!