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 realworld 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 and “DifferencesinDifferences” 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) Further topics: Quantile regression to conduct empirical analyses for particular subgroups (e.g. the median earner in the wage distribution), introduction to machine learning (a subfield of Artificial Intelligence)
The PC lab sessions will introduce you to coding and empirical analysis using the statistical software “R (studio)”, one of the most frequently used software packages. You will practically apply the methods of the lecture to realworld 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/econometricanalysiscrosssectionandpaneldatasecondedition
To a lesser extent, it is also based on the discussion in:
 Angrist, Joshua D. and JornSteffen Pischke. 2009. Mostly Harmless Econometrics. Princeton University Press.
Prerequisites
Introductory econometrics and statistics.
Evaluation
First attempt
 Exam:

Written 1h30 hours
 Documentation:
 Allowed
 Calculator:
 Allowed
 Evaluation:

You are evaluated based on two types of activities:

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.

Final exam. The final exam consists of an online multiple choice test in moodle (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.
Grading policy
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:
 Allowed
 Calculator:
 Allowed
 Evaluation:

Only the final exam (multiple choice test) can be retaken. The grades of the problem sets remain valid for the retake session.
