Empirical Economics 1

More up-to-date information regarding our courses in the present semester can be found on our German homepage.

Course Content

This course covers the core methods of econometrics, which is the connection of statistical estimation methods and economic theory. Econometric methods are used to empirically test the predictions of theoretical models (both from economics as well as from business administration) and to make statistically sound predictions of the economic decision making of individuals, households, and firms.
After a brief repetition of the fundamentals of statistics, we introduce the linear regression model. Starting with the case of one explanatory variable, we then extend the model to multiple explanatory variables. After covering the principles of the linear regression model, it’s practical application and potentially arising issues, we continue with the analysis of experimental data, models for binary dependent and independent variables as well as with methods for statistical inference in microdata applications.
Practical programming work is an essential component of this course and the tutorial classes will teach the basics. All datasets required for the problem sets will be supplied and the software used is open source and available at no cost.

Outline

1. Introduction
2. Econometric methods in current economic research
3. The linear regression model with one regressor
4. The linear regression model with multiple regressors
5. Non-linear relationships
6. Experiments and “natural” experiments
7. Binary dependent variables
8. Heteroskedasticity
9. Summary and outlook

Software

Gretl (until summer 2018), R (since winter 2018)

Textbook

- Jeffrey M. Wooldridge: Introductory Econometrics, 6th Edition. Boston, Mass. 2016.
- Florian Heiss: Using R for Introductory Econometrics. Düsseldorf 2016

Prerequisites

Statistics I and II; basic knowledge of the statistical software used