Courses

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.

Instructor:
Monday,
02:00pm to 04:00pm
at HU Berlin, Spandauer Str. 1, room 220
Thursday,
10:00am to 12:00pm
at HU Berlin, Spandauer Str. 1, room 203
Description:

The course introduces econometric methods for analyzing cross-sectional data, panel data and time series data and discusses their applicability in practice. The following topics are covered: extensions and applications of the linear model; instrumental variable estimation; binary response models; truncated and censored regression, static panel data models; specification, estimation, validation and forecasting of autoregressive models. The application of these methods is explained and illustrated by means of empirical examples.

Literature:
Marno Verbeek: "A Guide to Modern Econometrics", 2012, John Wiley & Sons.
James H. Stock, Mark W. Watson: "Introduction to Econometrics", 2007, Pearson Education.
Christiaan Heij, Paul de Boer et al.: "Econometric Methods with Applications in Business and Economics", 2004, Oxford University Press.

Exam:
Written exam (90 min)

Credits:
9.00
Click here to get more information or to sign up
Instructor:
Tuesday,
12:00pm to 02:00pm
at HU Berlin, Spandauer Str. 1, room 201
Monday,
10:00am to 12:00pm
at HU Berlin, Spandauer Str. 1, room 201
Description:

Estimation and testing in the general linear model, generalized least squares estimation, asymptotic theory, maximum likelihood estimation and likelihood based testing, nonlinear regression models, stochastic regressors, instrumental variable estimation, (generalized) method of moments.

Please note that this course can only be attended as BSE Core Course, if you have successfully applied for the Econometrics Beginner's Track (PhD Track in Economics). More information can be found in the BSE Handbook.

Literature:
Davidson, R. and MacKinnon, J.G. (2004): Econometric Theory and Methods, Oxford University Press.
Hayashi, F. (2000): Econometrics, Princeton University Press.

Exam:
Written exam (150 min)

Credits:
9.00
Click here to get more information or to sign up
Instructor:
Friday,
09:00am to 01:00pm
at FU Berlin, Garystr. 21, Room 104
Monday,
09:00am to 11:00am
at DIW Berlin, Mohrenstr. 58
Description:

Part I of the course (Anton Velinov) covers commonly used estimation techniques, such as Ordinary Least Squares, Maximum Likelihood, Generalized Least Squares. The Generalized Method of Moments framework is introduced and several popular estimators (IV, 2SLS, 3SLS, FE, RE) are derived from it.

Part II (Lars Winkelmann) provides a survey of the theory of time series methods in econometrics. Topics include univariate stationary and non-stationary models, vector autoregressions, cointegration, high-dimensional predictive models and volatility models.

Literature:
tba

Time and venue:
Lectures: Fridays, 9:00-13:00 at FU Berlin, Garystr. 21, Room 104
Tutorials: Mondays, 9:00-11:00 at DIW Berlin, Mohrenstr. 58, room tba

Exam:
tba

Credits:
9.00
Click here to get more information or to sign up
Instructor:
Wednesday,
08:30am to 12:00pm
at DIW, Mohrenstr. 58, Elinor-Ostrom Hall (1.2.019)
Description:

The objective of this course is to teach M.A. and Ph.D. students to use macroeconomic concepts and techniques for their own research and incorporates a higher degree of formal analysis than in the introductory master's lecture (IAMA).

Part I (Prof. Burda): Methods of modern macroeconomics for researchers in the field. Stationary Markov environments, state-space methods, stochastic difference equations. Dynamic programming and Lagrangian methods, complete markets, dynamic stochastic general equilibrium models, solution techniques.

Part II (Prof. Weinke): Dynamic stochastic general equilibrium (DSGE) models for positive and normative macroeconomic analysis. To this end a number of theoretical and empirical concepts are presented: The computation of impulse response functions, structural vector autoregressions, as well as an introduction to structural estimation. On the normative side the concept of Ramsey optimal policy is presented.

Literature:
Reference list (Prof. Burda): Ljungqvist and Sargent, Recursive Macroeconomics, 4th edition (MIT Press, USA: 2018); selected journal articles available on moodle.
Reference list (Prof Weinke): Selected articles, e.g., Galí, Jordi and Pau Rabanal (2004), Technology Shocks and Aggregate Fluctuations: How Well Does the RBC Model Fit Postwar U.S. Data?, in: NBER Macroeconomics Annual. Any further documents needed for the lecture will be available on moodle.

Exam:
Written exam (90 min)

Credits:
9.00
Click here to get more information or to sign up
Thursday,
09:00am to 12:00pm
at ESMT, Schlossplatz 1
Description:

Part 1: Henry Sauermann ‘The economics and sociology of science’

Part 2: Linus Dahlander ‘Management of innovation’

Literature: please see syllabus
Exam: paper presentation/term paper

Credits:
9.00
Click here to get more information or to sign up
Instructor:
Monday,
12:00pm to 04:00pm
at TU Berlin, Straße des 17. Juni 135, Main Building, room H 0106
Thursday,
02:00pm to 04:00pm
at HU Berlin, Spandauer Str. 1, room 21a
Friday,
04:00pm to 06:00pm
at HU Berlin, Spandauer Str. 1, room 21b
Description:

This course is devoted to the core elements of microeconomics. We study both the economics of households and the economics of firms and introduce general equilibrium with particular attention to the two welfare theorems. We also examine decisions under uncertainty, introducing expected and non-expected utility theories. The analysis of choice under uncertainty leads to the examination of financial markets and to strategic interaction problems, which we introduce through the key notions in noncooperative game theory, in particular Nash equilibrium and its most important refinements. Also matching problems will be discussed.

Literature:
Mas-Colell, A., Whinston, M.D. and J.R. Green (1995), Microeconomic Theory, Oxford University Press

Time and venue:
Lectures: Mondays, 12:00-16:00, TU Berlin, Straße des 17. Juni 135, Main Building, room H 0106
Tutorials: Thursdays, 14:00-16:00, HU Berlin, Spandauer Str. 1, room 21a or Fridays, 16:00-18:00, HU Berlin, Spandauer Str. 1, room 21b

Exam:
4 written midterms and 1 written final exam

Credits:
9.00
Click here to get more information or to sign up
Instructor:
Friday,
10:15am to 11:45am
at HU Berlin, Spandauer Str. 1, room 21b
Description:

In the seminar, the theoretical foundations of machine learning will be discussed. Topic include probably almost correct learning, VC dimension, risk minimization, boosting, model selection, stochastic gradient descent, support vector machines, kernel methods, and neural networks. After an introduction to the general topic of machine learning, students will present a chapter in the book “Understanding machine learning” by Shalev-Shwartz and Ben-David (Cambridge Universit Press) and hand in a short summary of the key findings. Participation in the discussions is expected.

Literature:
“Understanding machine learning” by Shalev-Shwartz and Ben-David (Cambridge Universit Press)

Exam:
Presentation and portfolio (30,000 characters). The portfolio examination consists of a research project in which the students show their learning progress.

Credits:
9.00
Click here to get more information or to sign up
Subscribe to Courses