Please check with the BSE Handbook which mandatory courses you have to choose in your PhD track. Not all courses listed here can be approved as Core Courses for all BSE PhD tracks.


This course provides a rigorous review of basic linear regression and techniques both for cross-sectional and panel applications. The course then covers further topics which are important in applied econometric analysis based on individual level data and longitudinal data. These topics includes a discussion of the asymptotic theory for nonlinear estimation techniques (MLE, Nonlinear Least Squares), discrete choice models, limited dependent variables models, and linear quantile regressions. The course provides an up-to-date treat-ment at the level of Wooldridge's textbook on “Econometric Analysis of Cross Section and Panel Data”. The course will regularly discuss the causal interpretation of econometric estimates. The focus of the course is both on understanding the methodological concepts and on how to apply them. Students will learn to implement the estimation methods using the econometric package Stata.

Knowledge of econometrics at the level of the courses “Econometric Methods 1” (First Master course) or “Econometrics I” (BDPEMS).

Tuesday 8:30-10:00, SPA 1, 202
Tuesday 14:00-16:00, SPA 1, 203 and 025 (PC-Pool)

1. Review of the linear Regression Model for Cross-Sectional Data
References: WO Chapters 1–6, CT Chapters 4, 21, 22, AP Chapters 2–4

1.1 Preliminaries: Conditional Expectations in Econometrics, Causal Analysis, Linear Projections
1.2 OLS: Asymptotic Theory, Robust Standard Errors, Partitioned Regression, Gauss-Markov-Theorem, Testing
1.3 Instrumental Variable Regression

2. System Estimation by OLS and GLS, Linear Panel Data Models
Reference: WO Chapters 7, 10, AP Chapter 5

3. Nonlinear Least Squares and Maximum Likelihood
Reference: WO Chapters 12, 13

4. Binary Response Models and Limited Dependent Variables
Reference: WO Chapters 15, 17

5. Linear Quantile Regression (QR)
References: KO, AP Chapter 7, WO Chapter 12.10, CT Chapter 4.6

5.1 Introduction to linear quantile regression: Distance function, Asymptotic distribution, Properties of the estimator, Interpretation as Method-of-Moments Estimator,
5.2 Decomposition Analysis with Quantile Regression and Unconditional Quantile Regression

Main References:
• AP: Angrist, J. D. and J.-S. Pischke (2009): Mostly Harmless Econometrics – An Empiricist’s Companion, Princeton University Press.
• CT: Cameron, A. C. and P. K. Trivedi (2005): Microeconometrics – Methods and Applications, Cambridge University Press.
• GR: Greene, W. (2008): Econometric Analysis, 6th ed., International Edition, Prentice Hall.
• KO: Koenker, R. (2005) Quantile Regression. Econometric Society Monograph, Cambridge University Press, Cambridge.
• WO: Wooldridge, J. M. (2010): Econometric Analysis of Cross Section and Panel Data. 2nd edition, Cambridge, MA: MIT Press (see also:

The grade will be based on a written final exam (90 minutes, two dates).

Further Information

There will be problem sets with theoretical and empirical exercises which will be assigned as voluntary homeworks for Master students and as mandatory homeworks for PhD students. Homeworks are to be submitted by groups of 2-4 students. The homeworks will be corrected for all students and the same number of credits will be given for all group members. However, the homeworks do not count as part of the final grade for master students. PhD students must obtain at least 50% of all possible credits for the graded homeworks in order to be able to write the final exam but the final grade for the PhD students will only be based on the final exam.

Further references, particularly regarding the method of Quantile Regression and the application of the methods, will be given in the course. The basic estimation techniques will be implemented in the PC Pool using the econo¬metric package Stata.

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Wednesday, 08:30am at TU, Strasse des 17. Juni 135, room H 0107

This course is divided into two parts:

The first part by Frank Heinemann analyzes how future expected money supply affects the current price level, why money can be written in the utility function and what is required to determine a unique equilibrium with rational expectations. Turning to the foundations of New Keynesian Macroeconomics, we analyze why monopolistic competition leads to an active role for monetary policy, derive the forward looking Phillips curve and study optimal monetary policy.

The second part (starting June 15) of this course deals with search and matching and is taught by Mathias Trabandt from Free University. The syllabus and organizational details about the second part of the course will be published in due course at

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09:00am to 12:00pm
at ESMT, Schlossplatz 1

Management Science II

This course familiarizes students with classical statistical methods of management research and theoretical models in industrial organization and strategic management.

Please see syllabus attached.

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This course is devoted to market failures and welfare economics. The first part focuses on the three classical conditions under which market outcomes lead to an inefficient allocation of resources: externalities, imperfect competition and asymmetric information. It addresses these questions both from a positive and normative perspective. The second part addresses fundamental issues of welfare economics from the perspective of a policy maker who designs and implements collective decisions. It focuses in particular on social choice theory, the foundations of bargaining and welfare economics, and mechanism design. The intention of the course is to familiarize students with the standard tools of modern economic theory and to train them in applying these tools to actual economic problems.

Literature: Mas-Colell, Whinston, and Green (1995), Microeconomic Theory, Part III and Part V

Exam: Final Exam: 18.7.2016

Mondays, 12:00pm to 04:00pm

18.4 Matthias Lang
25.4 Matthias Lang
2.5 Matthias Lang
9.5 Matthias Lang
16.5 no lecture
23.5 Helmut Bester
30.5 Helmut Bester
6.6 Helmut Bester
13.6 Helmut Bester
20.6 Roland Strausz
27.6 Roland Strausz
4.7 Roland Strausz
11.7 Roland Strausz
18.7 Exam

April 18 – June 13 at FU Berlin, Boltzmannstr. 20, Room HS 328
June 20 – July 18 at HU Berlin, Spandauerstr. 1, Room HS203

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