<p>Lecture, three hours; discussion, one hour. Preparation: background in undergraduate econometrics; and a solid foundation in LaTeX, Jupyter Notebook, and R Markdown which is presented in the program data analysis and mathematics bootcamps for those who lack familiarity. Rigorous and practical introduction to econometric methods at the graduate level, with a strong emphasis on implementation using Python and mathematical derivations. Students build a solid foundation in classical linear regression, diagnostic testing, and model specification, progressing to more advanced topics such as panel data analysis, time series econometrics, and causal inference. Through applied coding exercises and real-world data analysis, students learn to formulate, estimate, and interpret econometric models for academic research and policy evaluation. Letter grading.</p>

Economics

College of Letters and Science

ECON 430A - Advanced Applied Econometrics with Python

Advanced Applied Econometrics with Python

Apply panel and time series methods to economic data.

Derive and interpret classical and modern econometric models.

Evaluate causal relationships using quasi-experimental methods.

Diagnose and address violations of model assumptions.

Critically assess econometric findings in academic literature.

Implement econometric techniques using Python and relevant data analysis libraries.

General Catalog

Advanced Applied Econometrics with Python

ECON 430A

1 to 4 units