Advanced Applied Econometrics with Python

1 to 4 units · Graduate professional courses (400-499)

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.