Courses

Wednesday,
10:00am to 12:00pm
at HU Berlin, Dorotheenstraße 1, Room 005
Description:

This course provides a theoretical and empirical treatment of major topics in corporate finance, including capital structure, investment decisions, corporate governance, corporate cash and payout policy, as well as credit ratings and financial regulation. The course is based on academic articles and designed for Ph.D. students interested in corporate finance. An integral part is the computer lab where students implement key models and econometric methods in GNU/R.

Literature:
Academic articles

Exam (written?):
No final exam. Grading is based on the contribution to class discussions (20%), presentation of research paper (15%), lab code (25%), seminar paper (40%).

Credits:
6.00
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Instructor:
Monday,
10:00am to 02:00pm
at SPA1, R22
Description:

Further description will follow.

Credits:
6.00
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Instructor:
Wednesday,
10:00am to 12:00pm
at Freie Universität, HS 104a (tba)
Description:

The aim of the course is to teach students how to interpret empirical research in public economics and to apply modern econometric methods commonly used in the field. The course covers alternative empirical approaches and important topics in empirical public economics. Empirical approaches include both structural and non-structural estimation methodologies.

Topics include: The measurement of the distribution effects of taxes and transfers, treatment effects estimation of policy reforms, structural estimation of labor supply models with taxes, and the empirical ex-ante evaluation of tax-benefit reforms. The course assumes knowledge of applied microeconometrics.

Literature: Journal articles
Exam (written?): 2 hours final exam; term paper

Credits:
6.00
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Instructor:
Tuesday,
08:00am to 10:00am
at SPA 1, 22
Tuesday,
02:00pm to 04:00pm
at SPA1, 22
Description:

This course presents nonparametric and semiparametric regression techniques and modern microeconometric methods for treatment effects estimation. The treatment focuses on the potential outcome approach, and students learn various methods to account for selection based on observables (regression, matching, inverse probability weighting) and for selection based on unobservables (Heckman selection correction, difference-in-differences, panel regression, instrumental variable regression, regression discontinuity design). These methods are used for cross-section data and longitudinal data, both repeated cross-sections and panel data. Students will familiarize themselves with applying the methods to real empirical data using Stata.

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.
HL: Härdle, W. and O. Linton (1994): "Applied Nonparametric Methods", in: Handbook of Econometrics, Vol. 4, R. F. Engle und O. F. McFadden, (eds.), Elsevier Science.
PU: Pagan, A. and A. Ullah (1999): Nonparametric Econometrics, Cambridge University Press.
WO: Wooldridge, J. M. (2010): Econometric Analysis of Cross Section and Panel Data. 2nd edition, Cambridge, MA: MIT Press (see also: http://mitpress.mit.edu/books/econometric-analysis-cross-section-and-pan... ).

Further references, particularly regarding the method of Quantile Regression and the application of the methods, will be given in the course.

Exam: written exam (90 min)

Credits:
6.00
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Wednesday,
10:00am to 12:00pm
at SPA1, R23
Description:

What determines business cycle fluctuations? How can we make causal statements in macroeconomics in general? In this course, students will study concepts, methods and techniques used in empirical macroeconomics. Therefore, it will be a good complement to advanced macroeconomics courses. The course consists of three blocks (see tentative schedule below). In the first block, the course covers basic time series models, estimation and inference methods and forecasting. In the second block, the course will introduce students to the identification of causal effects in macroeconomic time series through restrictions coming from economic theory and/or other information. The third block, subject to time availability, will be devoted to more advanced topcs. Students will also learn how to program in Matlab.

The course consists of a weekly lecture throughout the semester (2SWS). A repetition section (2SWS) will cover analytical and computer exercises. Due to the extensive nature of the material covered, successful completion of the course makes it essential to attend class regularly. Lectures will be in English.
Pre-requisites: IAMA/Advanced Monetary Economics/other advanced macroeconomics courses and Introduction to Econometrics/Macroeconometrics (BSc). In general, students should have taken standard undergraduate level econometrics and be knowledgeable in basic probability and modern dynamic macroeconomic models (DSGE). Some prior knowledge of scientific programming is desirable but not essential for successful completion of the course.

Aims/Outcomes: Upon successful completion of this course, the student should be able to:
(a) Communicate and explain key concepts in (time series) macroeconometrics.
(b) Specify, estimate and critically assess vector autoregressive models.
(c) Understand the concept of identification and the link between DSGE models and the data.
(d) Formulate and solve macroeconometric problems with computer software.
(e) Develop further analytical and computational skills.
(f) Appreciate the differences between empirical approaches to tackle macroeconomic questions.

The following books cover most of the material. Further references/readings will be provided during the lecture.

*Canova, F. (2007) “Methods for Applied Macroeconomic Research”, Princeton University Press
*Hamilton, J.D (1994), Time Series Analysis, Princeton U Press
*Kilian, L and Lütkepohl, H (2017), Structural Vector Autoregressive Analysis (online)
*Lütkepohl, H (2007), New Introduction to Multiple Time Series Analysis, Springer

Credits:
6.00
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Instructor:
Tuesday,
10:00am to 12:00pm
at Technische Universität Berlin, Straße des 17. Juni 135, Gebäudeteil Mechanik, room M 128 (lecture)
Friday,
02:00pm to 04:00pm
at Technische Universität Berlin, Straße des 17. Juni 135, Hauptgebäude, room H3004 (tutorial)
Description:

Please note that there is both a lecture and a tutorial:

Instructor(s): Marco Runkel (lecture), Zarko Kalamov (tutorial)
Time frame (date of first and last class): October 17, 2017 until February 13, 2018 (lecture), October 20, 2017 until February 16, 2018 (tutorial)
Weekday(s): Tuesday (lecture), Friday (tutorial)
Time(s): 10am – 12am (lecture), 2pm – 4pm (tutorial)
Location(s): Technische Universität Berlin, Straße des 17. Juni 135, Gebäudeteil Mechanik, room M 128 (lecture)
Technische Universität Berlin, Straße des 17. Juni 135, Hauptgebäude, room H3004 (tutorial)

English description of the course: given in the first session
Literature: given in the first session
Exam: final exam (90 minutes)

Credits:
6.00
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Instructor:
Friday,
10:00am to 12:00pm
at SPA1, R23
Description:

Reading group on mechanism design without transfers

Literature: Original papers on mechanism design without transfers
Exam (written?): No

Credits:
3.00
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Instructor:
Thursday,
10:00am to 12:00pm
at Do 202 Sitzungsraum / Kaminzimmer (Boltzmannstr. 16-20), Do HFB/K I Konferenzraum (Garystr. 35-37), Do Hs 108a Hörsaal (Garystr. 21)
Credits:
6.00
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