Microeconometrics

Description

In this module, we will discuss key modeling techniques for the analysis of cross-section and panel data on microeconomic units such as persons, households, firms or regions. After a review and extension of key concepts in statistics and basic econometrics, we will cover linear models for panel data that allow the analyst to incorporate unobserved unit specific effects. The final part of the lecture will focus on estimation methods for Big Data involving machine learning. In particular, we will discuss how to obtain sparse regression models from high-dimensional data. While the focus of this module is on the theory and implementation of microeconometric methods, we will also discuss selected economic applications such as the gravity equation of international trade, the economics of crime, and the return to education.

The course consists of lectures and exercises worth six credits in total (2+2 SWS). The lectures take place (approximately) twice a week, from the first week of the semester until mid-May. The exercises take place once a week, from the second until the last week of the semester

Syllabus

Lecture

Exercise