Course ID
041134
Course Description
Econometrics is the art and science of the estimating and testing of economic models. These estimated models can then be used for causal inference and prediction. This course gives a rigorous introduction in econometrics. It covers the linear model, potential outcome model, the average treatment effect, multivariate linear model, nonlinear models with and without endogeneity, LASSO estimation, machine learning, prediction, and the bootstrap. Knowledge of statistics at the level of Economics 510 Masters level is assumed as well as knowledge of calculus at the level of Hansen (2018), appendix A. Computer programming experience is helpful but not required. An important objective of the course is for the student to learn how to conduct--and how to critique--empirical studies in economics and related fields. The course emphasizes understanding and intuition so that you can adjust the tools to new quantitative problems that you may encounter. This distinguishes the course from an undergraduate course or an 'econometric cookbook' course.
Min Units
3
Max Units
3
Repeatable for Credit
No
Grading Basis
GRD - Regular Grades A, B, C, D, E
Career
Graduate
Course Attributes
GIDP - GC (Global Change)
Component
Lecture
Optional Component
No