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.

Monday,
02:00pm to 04:00pm
at HU Berlin, Spandauer Straße 1, Room 220
Thursday,
10:00am to 12:00pm
at HU Berlin, Spandauer Straße 1, Room 203/025
Wednesday,
02:00pm to 04:00pm
at HU Berlin, Spandauer Straße 1, Room 25
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.

Time and Venue
Lecture: Mondays 14:00-16:00 (weekly, from Nov 9), Spandauer Straße 1, Room 220 and Thursdays 10:00-12:00 (bi-weekly, from Nov 5) Spandauer Straße 1, Room 203
Tutorials: Wednesdays 14:00-16:00 (bi-weekly, from Nov 11), Spandauer Straße 1, Room 25 or Thursdays 10:00-12:00 (bi-weekly, from Nov 12) Spandauer Straße 1, Room 25

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:
Wednesday,
10:00am to 12:00pm
at HU Berlin, Spandauer Str. 1, Room 202
Wednesday,
12:00pm to 02:00pm
at HU Berlin, Spandauer Str. 1, Room 25
Thursday,
08:30am to 10:00am
at HU Berlin, Spandauer Str. 1, Room 125
Description:

This course is the substitute for the Core Course "Theory and Practice of Machine Learning" that cannot be offered this semester.

The module is concerned with theories, concepts, and practices to inform and support managerial decision making by means of formal, data oriented methods.

Topics & Content
Fundamentals of Business Analytics
Making data accessible: Tools for summarization, grouping, and visualization
The business case for predictive modeling
Prediction methods for regression and classification
Advanced data types: time series, text, survival, and network data Fundamentals of intelligent search
Further elaboration of lecturing material
Practical PC exercises

In the course we will work with R, a widely-used statistical programming languages. To participate in the course, a basic understanding of R is desirable. The Chair of Statistics is offering a course on Statistical Programming Languages each term. Taking that course is an excellent preparation for Business Analytics & Data Science. You can find more information at: https://lvb.wiwi.hu-berlin.de/Teaching_Moodle/ss15/63145

Time and Venue
Lectures: Wednesdays 10:00-12:00 Spandauer Straße 1, Room 202
Tutorials: Wednesdays 12:00-14:00, Spandauer Straße 1, Room 25 or Thursdays 08:30-10:00, Spandauer Straße 1, Room 125

Exam:
Written final exam (90 min)

Credits:
9.00
Click here to get more information or to sign up
Monday,
10:00am to 12:00pm
at HU Berlin, Spandauer Str. 1, Room 201
Tuesday,
12:00pm to 02:00pm
at HU Berlin, Spandauer Straße 1, Room 201
Thursday,
02:00pm to 04:00pm
at HU Berlin, Spandauer Straße 1, Room 202
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.

Time and Venue
Lecture: Mondays 10:00-12:00 (weekly, from Nov 9th) and Tuesdays 12:00-14:00 (weekly, from Nov 3rd), both: Spandauer Straße 1, room 201
Tutorials: Thursdays 14:00-16:00 (weekly, from Nov 5th) Spandauer Straße 1, room 202 or Fridays 12:00-14:00 (weekly, from Nov 6th) Spandauer Straße 1, room 22

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
Description:

Part I of the course (N.N.) 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 (N.N.) 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:
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
Instructor:
Thursday,
09:00am to 12:00pm
at ESMT, Schlossplatz 1
Description:

Part I: Henry Sauermann ‘The organization of science’

Part II: Stefan Wagner ‘Innovation, Intellectual Property Rights and the Market for Technology'

Science and innovation are central to economic growth, social welfare, and firms’ ability to create and capture value. This course will cover key issues in science and innovation, as seen from the perspective of management scholars, economists, and sociologists.

Schedule and Syllabus will follow soon.

Credits:
9.00
Click here to get more information or to sign up
Instructor:
Monday,
12:00pm to 04:00pm
at HU Berlin, Spandauer Str. 1, Room 203
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, HU Berlin, Spandauer Str. 1, Room 203
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
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