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.
Ch2. The Simple Regression Model
Ch3. Multiple Regression Analysis: Estimation
Ch4. Multiple Regression Analysis: Inference
Ch5. MRA - OLS Asymptotics
Ch6. MRA - Further Issues
Ch7. MRA - Qualitative Regressors
Ch8. Heteroskedasticity
Ch9. Specification and Data Issues
Ch10. Basic Regression Analysis with Time Series Data
Ch11. Further Issues in Using OLS with Time Series Data
Ch12. Serial Correlation and Heteroskedasticity in Time Series Regressions
How to Use¶
- Click on the “Open in Colab” badge next to the notebook you want to explore.
- The notebook will open in Google Colab, where you can run the code and interact with the content.
- 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.