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## Econometrics

• Teacher(s):   M.Huber
• Course given in: English
• ECTS Credits: 7.5 credits
• Schedule: Autumn Semester 2022-2023, 4.0h. course + 1.0h exercices (weekly average)
• sessions
• course website
• Related programmes:
Master of Science (MSc) in Economics

Master of Law (MLaw) in Law and Economics

### Objectives

This course discusses several of the practically most relevant econometric/statistical methods used in empirical work in economics and management. The course also discusses how to apply these methods in actual data using the statistical software “R (studio)”. The objective is to enable participants (1) to understand differences in the properties and assumptions of the various methods along with their advantages and disadvantages and (2) to apply econometric methods to real-world problems.

### Contents

The course consists of a lecture and PC lab sessions.

The lecture discusses important econometric methods along with their underlying assumptions and properties. The topics covered include:
1) The difference between causation (e.g. education has a causal effect on wage) and correlation (subjects with higher education have higher wages, but this may be driven by other factors than education as for instance ability); the intuition of experiments for assessing causation.
2) Linear regression (OLS - ordinary least squares) to assess the association of one or several variables (e.g. education, age,...) with an outcome of interest (e.g. wage).
3) Nonlinear regression (probit regression for binary outcomes like working vs. not working, tobit regression for censored outcomes),
4) Instrumental variable regression and regression discontinuity designs under endogeneity.
5) Panel data regression, “Difference-in-Differences”, and synthetic controls-based estimation when subjects are observed at several points in time.
6) Introduction to time series econometrics, e.g. for modeling stock prices or GDP growth over time.
7) Quantile regression to conduct empirical analyses at particular ranks of the outcome distribution (e.g. the median earner in the wage distribution).
8) Introduction to machine learning (a subfield of Artificial Intelligence for a data-driven development of statistical models) for prediction and causal analysis.

The PC lab sessions consist of empirical examples using the statistical software “R (studio)”, one of the most frequently used software packages. You will practically apply the methods of the lecture to real-world data in several problem sets. An introduction to “R (studio)” is provided in the first PC lab.

### References

The material covered in the lecture is primarily based on the following textbook:

• Jeffrey M. Wooldridge, 2010, "Econometric Analysis of Cross Section and Panel Data", MIT Press, Second Edition.

which can be bought or rented here:
https://mitpress.mit.edu/books/econometric-analysis-cross-section-and-panel-data-second-edition

To a lesser extent, it is also based on the discussion in:

• Angrist, Joshua D. and Jorn-Steffen Pischke. 2009. Mostly Harmless Econometrics. Princeton University Press.

### Pre-requisites

Introductory econometrics and statistics.

### Evaluation

#### First attempt

Exam:
Written 1h30 hours
Documentation:
Not allowed
Calculator:
Allowed
Evaluation:

You are evaluated based on two types of activities:

1. Problem sets. Four problem sets will be distributed during the course. You will have to solve a part of them either individually or in groups of up to 4 students and hand them in prior to a specific date. The problem sets will then be discussed in the PC lab sessions (including the part not be solved prior to the respective lab session). The points that can be obtained in the problem sets (4 points per set, 16 points in total) account for 20% of the total points of the course.

2. Final exam. The final (closed book) exam consists of a multiple choice test (1.5 hours) and makes up for 80% (or 64 points) of the total points of the course. It asks questions about the properties, intuition, interpretation, and/or underlying assumptions of the methods covered in the course and may also contain questions about specific calculations and the interpretation of regression output.

Your final grade is computed based on the sum of points obtained in the problem sets and in the final exam.

#### Retake

Exam:
Written 1h30 hours
Documentation:
Not allowed
Calculator:
Allowed
Evaluation:

Only the final exam (multiple choice test) can be retaken. The integrative exam accounts for 100% of the final grade.

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