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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.

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:
Main: Békés, Gábor and Gábor Kézdi: "Data Analysis for Business Economics and Policy", Cambridge Univ. Press, 2021, https://gabors-data-analysis.com/
Additional: Verbeek, Marno: "A Guide to Modern Econometrics", John Wiley & Sons, 2012.

Time & venue:
Lectures: Mondays, 12:00-15:00 (starting on 25.10.2021)
Tutorials: Mondays, 15:00-16:00 (starting on 25.10.2021)

Exam:
Written exam

More information about access to the course platform can be found on Moodle.

Credits:
9.00
Click here to get more information or to sign up
Instructor:
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. Students have the opportunity to develop a variety of
skills, including:

  • Students are familiar with the three branches of descriptive, predictive and prescriptive analytics and appreciate the relationships between these streams.
  • Given some data, students are able to select appropriate techniques to summarize and visualize the data so as to maximize managerial insight.
  • Students understand the potential and also the limitations of predictive analytics to aid decision making. They comprehend when and how business applications can benefit from predictive analytics. Given some decision task, they are able to recommend suitable prediction methods.
  • Students are familiar with statistical programming languages. Using standard tools, they can develop basic and advanced prediction models and assess their accuracy in a statistically sound manner.

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

The lecture is accompanied by a tutorial session, in which lecture topics are further elaborated. The aim of the tutorial is to develop and assess empirical models using contemporary data science software. More specifically, the R programming language is used in tutorial session. Students who are not familiar with R are given an opportunity to learn R/programming fundamentals in the first weeks of the tutorial sessions. In order to acquire the skills needed for the course in such short time frame, students must be prepared to invest ample time into self-study exercises.

Time & venue:
Lectures: Wednesdays, 10:00-12:00
Tutorials: Wednesdays 12:00-14:00, or Thursdays 08:30-10:00

Exam:
Written exam

More information can be found on Moodle.

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

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

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.

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

Time & venue:
Lectures: Mondays, 10:00-12:00 (starting on 25.10.2021), and Tuesdays, 8:00-10:00 (starting on 19.10.2021)
Tutorials: Thursdays, 14:00-16:00 (starting on 21.10.2021), or Fridays 12:00-14:00 (starting on 22.10.2021)

Exam:
Written exam

More information can be found on Moodle. Moodle key: ...

Credits:
9.00
Click here to get more information or to sign up
Instructor:
Description:

Part I (Anton Velinov): The first part of the course covers selected topics fundamental to understand and apply econometric methods in research. These topics include: revision of OLS and asymptotics, single and multiple equation GMM, panel data methods, and state space models. For more information about the course content and organization, please visit http://avelinov.byethost7.com/econ_meth.html (materials will be updated soon).
 

Part II (Simone Maxand): The second part of the course will provide survey of the theory of time series methods in (advanced) econometrics. We will cover (classical) topics including univariate stationary and non-stationary models, vector autoregressions, vector error correction models and both univariate and multivariate models for volatility. Empirical applications in the course will be drawn from macroeconomics. For more information about the course content and organization, please visit Moodle (materials will be updated soon).

Literature:
tba

Time & venue:
Lectures: Fridays, 10:00-14:00 (starting on 22.10.2021); HU, School of Business and Economics, Spandauer Str. 1, room 203
Tutorials: Mondays, 9:00-11:00 (starting on 25.10.2021); HU, School of Business and Economics, Spandauer Str. 1, room 203

Exam:
tba

Credits:
9.00
Click here to get more information or to sign up
Instructor:
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, statespace methods, stochastic difference equations. Dynamic programming and Lagrangian methods, complete markets, dynamic stochastic general equilibrium models, solution techniques. The Ramsey problem.

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.
Reference list (Prof Weinke): Selected articles.

Time & venue:
Lectures: Wednesdays, 8:30-12:00

Exam:
Written exam

More information can be found on Moodle.

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

Management Science I

Part 1: The organization of science
Instructor: Henry Sauermann

Part 2: Innovation, intellectual property rights and the market for technology
Instructor: Stefan Wagner

Please see schedule and syllabus for Part 1 attached.

Credits:
9.00
Click here to get more information or to sign up
Instructor:
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 & venue:
Lectures: Mondays, 12:00-16:00 (starting on 25.10.2021)
Tutorials: Thursdays, 14:00-16:00 (starting on 28.10.2021), or Fridays, 16:00-18:00 (starting on 29.10.2021)

Exam:
4 written midterms and 1 written final exam

More information can be found on Moodle.

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