Microeconometrics and Machine Learning (MSc & PhD) (2022)

Course Description and Outline

Microeconometric research has substantially contributed to the scientific evaluation of public policies and the identification of causal mechanisms in economics. The goal of this course is to make students acquainted with major econometric techniques used in empirical research using micro-level data on individuals, households, firms, and many more.

The course covers traditional econometric methods as well as modern machine learning tools to analyze cross-sectional and panel data. Special emphasis will be given to the fundamental concepts of causal inference, experiments, and policy interventions. By writing their own codes in Python or R students will learn how to apply the toolbox using real world data.

  1. Quantal Response Models
  2. Limited Dependent Variable Models and Sample Selection Issues
  3. Model Selection and Regularization Strategies
  4. Supervised Machine Learning Approaches
  5. Classical Causal Inference
  6. Machine Learning and Causality

Course Materials and Time Schedule

The course material is provided via ILIAS. You have to register for the course via Zeus and will be automatically added to the ILIAS folder.

Thursday 17:00 - 18:30 F 425  
Friday 10:00 - 11:30 F 425 starting 22 April 2022

On Fridays, lectures and tutorials take place alternately.

Prerequisites

Econometrics I (required)

Advanced Econometrics (desirable)

Introductory Literature

An Introduction to Statistical Learning with Applications in R,  G. James, D. Witten, T. Hastie & R. Tibshirani, 2nd ed, Springer, 2021

Causal Inference for Statistics, Social, and Biomedical Sciences, G.W. Imbens & D.R. Rubin, Cambridge University Press, 2015.

Econometric Analysis of Cross-Sectional and Panel Data, J. M. Wooldridge, 2nd ed. MIT Press, 2010

Regression for Categorial Data, G. Tutz, Cambridge University Press, 2012

Grading

The grading is based on a presentation of programming exercises (20%) and a final exam (80%).