Course Description
The main goal of this course is to provide a rigorous introduction to the field of statistical inference and its applications to economics and econometrics. The treatment is both mathematically rigorous and practical. Along with classic topics such as large sample theory, likelihood and generalized inference, research topics such as inference in high dimensional (factor) models and bootstrap inference will be introduced and discussed. At the end of the course, students should have acquired the set of basic statistical tools necessary to conduct inference in a large number of econometric models.
Topics
Introduction to Statistical inference (GC): point and interval estimation, hypothesis testing, bootstrap methods
Introduction to large sample theory (GC): modes of convergence, law of large numbers and central limit theorems
Inference in statistical and econometric models (MB): Maximum Likelihood estimation, the Generalized method of moments
Multivariate and high dimensional models (MB): Vector autoregressions and factor models
Prerequisites: Prior knowledge of chapters 1-6 in Hansen (2020), Introduction to Econometrics (downloadable at https://www.ssc.wisc.edu/~bhansen/probability/), is required
Teaching methods: Lectures
Assessment methods: Take home assignment
Readings:
Hansen (2020), Introduction to Econometrics, chapters 7-14 (downloadable at https://www.ssc.wisc.edu/~bhansen/probability/)
Hansen (2020), Econometrics, chapters 6-10, 12-15 (downloadable at https://www.ssc.wisc.edu/~bhansen/econometrics/)