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.

Description:

Estimation and testing in the general linear model, generalized least squares estimation, asymptotic theory, nonlinear regression models, stochastic regressors, instrumental variable estimation, 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.
Credits:
9.00
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Instructor:
Description:

The course deals with advanced estimation techniques in modern econometrics and standard single equation and systems of equations models. It covers time series as well as microeconometric methods.

Credits:
9.00
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Instructor:
Description:

The course aims at providing the basic concepts and methods for analysing time series data. The focus is on univariate modelling tools. The lecture begins with classical components models. Then we cover different types of stochastic processes like ARIMA and GARCH models, deal with the unit root methodology and procedures for forecasting as well as for the specification, estimation and validation of models. Multivariate extensions are demonstrated, with emphasis on vector autoregressive (VAR) processes and its application in causality and impulse response analyses. Nonstationary systems with integrated and cointegrated variables will also be treated.
In the tutorials the time series methods are applied to empirical data. We will intensively make use of econometric software packages. A deeper insight into advanced methods and additional topics is offered by means of assignments, empirical studies and/or literature reviews.

Literature:
Hamilton (1994). Time Series Analysis. Princeton, University Press.
Schlittgen/Streitberg (2001). Zeitreihenanalyse. Oldenburg Verlag, München.
Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer Verlag, Heidelberg.

Credits:
9.00
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Description:

This course deals with advanced estimation techniques in modern econometrics.
Main topics include generalized methods of moments (GMM) estimation for single-equation models and multiple-equation models, information theoretic approaches as well as pseudo-maximum likelihood techniques. Furthermore, an introduction to Bayesian econometric methods will be given. Here the focus is on fundamental principles of Bayesian inference, Markov chain Monte-Carlo (MCMC) methods as well as different applications of Bayesian inference. The third and forth part covers non- and semiparametric methods in econometrics. We will study basic Kernel density estimation, nonparametric regression techniques and estimation of partially linear and additive models. A deep knowledge of the techniques conveyed in this course is extremely useful since they are applied in various areas in modern econometrics, including time series econometrics, micro econometrics, panel econometrics as well as financial econometrics.

Literature:
Amemiya, T. (1985): Advanced Econometrics. Basil Blackwell, Oxford.
Greenberg, E. (2008): Introduction to Bayesian Econometrics, Cambridge University Press.
Gouriéroux, C. and Monfort, A. (1995): Statistics and Econometric Models, Cambridge University Press, Vol. 1 and 2.
Hansen, L. P. (1982): “Large Sample Properties of Generalized Method of Moment Estimators”, Econometrica, 50, 1029–1054.
Hayashi, F. (2000): Econometrics, Princeton University Press.
Koop, G. (2003): Bayesian Econometrics, Wiley.
Li, Q. and Racine, J. (2007): Nonparametric Econometrics, Princeton University Press.
Newey, W. K. (1993): “Efficient Estimation of Models with Conditional Moment Restrictions”, in Handbook of Statistics, ed. by G. S. Maddala, C. R. Rao, and H. D. Vinod, pp. 419–454. Elsevier Science.
Newey, W. K. and McFadden, D. (1994): “Large sample estimation and hypothesis testing”, Handbook of Econometrics, Volume IV.
Pagan, A. and Ullah, A. (1999): Nonparametric Econometrics, Cambridge University Press.

Credits:
9.00
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Instructor:
Description:

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, empirical consequences of macroeconomic shocks; structural estimation, the Ramsey problem, dynamic stochastic general equilibrium (DSGE) models. To this end a number of theoretical and empirical concepts are presented. Examples include 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:
Ljungqvist and Sargent, Recursive Macroeconomics, 2nd edition (Cambridge, USA: 2004)
Selected journal 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.

Credits:
9.00
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Instructor:
Description:

1. Money and Inflation - The Cagan Model of Money and Prices
2. Money in the Overlapping-Generations Model
3. Modelling Money in the Utility Function (Brock/Sidrauski Model, Cash-in advance model, shopping-time model)
4. Monopolistic Competition and Price Rigidities
5. The New Neoclassical Synthesis and Monetary Policy
6. Commitment and Discretion in Monetary Policy
7. Stabilizing Demand and Supply Shocks
8. Current Developments and Open Issues

Literature:
Blanchard, Olivier J.; Stanley Fischer, Lectures on Macroeconomic, MIT Press, 1989.
Walsh: Monetary Theory and Policy, MIT-Press, 2003.
Woodford: Interest and Prices: Foundations of a Theory of Monetary Policy, Princeton University Press, 2003.

Credits:
9.00
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Instructor:
Description:

The theories and methods of social network analysis have increasingly been harnessed to better understand a diverse array of topics, such as the spread of obesity, the diffusion of innovations, mobility in labor markets, risk-taking behavior in tournaments, and affiliation-based market signaling. This course offers an introduction to the theoretical perspectives and quantitative methods of the network-analytic tradition. A number of key concepts will be introduced, together with opportunities to apply corresponding methods and approaches to measurement using data made available in class. The literature on networks is approached with two goals in mind: (1) understanding the foundations of social network theory and (2) applying methods.

Credits:
9.00
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Instructor:
Description:

The course Management Science II is divided into two consecutive modules: The first module encompasses intellectual property rights and the market for technology while the second module covers marketing models. Time I and Venue I above refer to the first module, Time II and Venue II to the second module.

Credits:
9.00
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Instructor:
Description:

This course is devoted to the economic theory of preferences and choice, consumer choice, demand, production, market equilibrium, decision making under uncertainty, and game theory. The intention of the course is to familiarize students with the advanced tools of modern microeconomic theory.

Literature:
Mas-Colell, Whinston, Green: Microeconomic Theory (1995).

Credits:
9.00
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Instructor:
Description:

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

Credits:
9.00
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