ECON 430A
Advanced Applied Econometrics with Python
Economics · 1 to 4 units · Graduate professional courses (400-499)
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.
When it runs
Checking the Schedule of Classes…
Requisites
Official UCLA wording
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.
BruinTree reads · Preparation
needs reviewconfidence 0.00 · from textBruinTree could not read this requirement — see UCLA’s wording above.
- · could not read "background in undergraduate econometrics" (no course number found)
- · could not read "a solid foundation in LaTeX" (no course number found)
- · could not read "Jupyter Notebook" (no course number found)
- · could not read "R Markdown which is presented in the program data analysis" (no course number found)
- · could not read "mathematics bootcamps for those who lack familiarity" (no course number found)
- · no course reference could be read from this requisite
Requires
Everything that has to come before this course, not just the courses named in the requisite above.
Nothing — this is an entry point.
Unlocks
What this course is a requisite for, and what those courses lead to in turn.
No course in the catalog lists this as a requisite.