Welcome#
… to this interactive web-based Jupyter Book, designed to guide you through the process of replicating examples from Wooldridge’s renowned textbook, “Introductory Econometrics: A Modern Approach. 6ed.” This resource aims to enhance your understanding of econometric concepts and techniques by providing codes using Python.
I hope this resource serves as a valuable companion to Wooldridge’s textbook, making econometrics more accessible and enjoyable through the power of Python.
For those interested in Stata and/or R versions of the replication exercises, please refer to the following links, Stata and R, respectivelly.
If you have any questions, inquiries, or suggestions, or wish to report an error, please don’t hesitate to reach out via this link.
Check out the content pages bundled with this sample book to see more.
Happy learning!
- Chapter 2. The Simple Regression Model
- Chapter 3. Multiple Regression Analysisl
- Chapter 4. Multiple Regression Analysis: Inference
- Chapter 5. Multiple Regression Analysis: OLS Asymptotics
- Chapter 6. Multiple Regression Analysis: Further Analysis
- Chapter 7. Multiple Regression Analysis with Qualitative Information
- Chapter 8. Heteroskedasticity
- Chapter 9. More on Specification and Data Issues
- Chapter 10. Basic Regression Analysis with Time Series Data
- Chapter 11. Further Issues in Using OLS with Time Series Data
- Chapter 12. Serial Correlation and Heteroskedasticity in Time Series Regressions
- Chapter 13. Pooling Cross Sections across Time
- Chapter 14. Advanced Panel Data Methods
- Chapter 15. Instrumental Variables Estimation and TSLS
- Chapter 16. Simultaneous Equations Models
- Chapter 17. Limited Dependent Variable Models and Sample Selection
- Chapter 18. Advanced Time Series Topics