Courses

This course covers modern methods of estimation and inference in econometrics. Topics include maximum likelihood and pseudo-maximum likelihood estimation, the (generalized) method of moments, instrumental variables, simulation-based methods (simulated likelihood and method of simulated moments, bootstrap), non- and semiparametric estimation, and an introduction to machine learning with a focus on Lasso and Ridge regression. The course gives students the theoretical background for further courses in econometrics and for empirical research. Tutorials combine theoretical exercises with empirical applications in R.Prerequisites: introductory econometrics, basic probability theory and linear algebra.

Tentative Outline

1. Introduction
2. Maximum Likelihood and Pseudo-Maximum Likelihood
3. Method of Moments and GMM
4. Instrumental Variables
5. Simulation-Based Methods
6. Non- and Semiparametric Estimation
7. Machine Learning Basics: Lasso and Ridge Regression

Literature

• Cameron, A.C. and Trivedi, P.K. (2005): Microeconometrics: Methods and Applications. Cambridge: Cambridge University Press.
• Hansen, B.E. (2022): Econometrics. Princeton: Princeton University Press. (A free manuscript version is available at www.ssc.wisc.edu/~bhansen/econometrics.)
• Hastie, T., Tibshirani, R. and Friedman, J. (2009): The Elements of Statistical Learning, 2nd ed. New York: Springer.
• Wooldridge, J.M. (2010): Econometric Analysis of Cross Section and Panel Data, 2nd ed. Cambridge, MA: MIT Press.