Teaching
Summer term (2024)
10146006-11 Financial Econometrics (MA: Economics, Statistics)
This course introduces students to financial statistics. Selected topics include extreme value theory, volatility modeling, high-frequency statistics, large dimensional factor modeling and forecast evaluation. The course requires a solid background in statistics and mathematics and some knowledge of economics and finance. Examples drawn from risk management and portfolio management will highlight the practical relevance of the statistical methods. The main objectives are to give students a background that will enable them to understand and critically appraise applied work on financial issues, and to provide students with some practical experience in working with financial data.
10192851 Topics in Time Series Econometrics (PhD: BDPEMS/DRS/DIW)
This seminar is intended to give participants exposure to state of the art research in time series econometrics and its applications in empirical finance and macroeconomics. Doctoral students are encouraged to present first research projects in these areas during the seminar.
Winter term (2023/24)
10140201 Econometric Analysis I (MA: Economics, Statistics)This course covers the statistical methods needed to understand empirical economic research and to plan and execute independent research projects. Topics include regressions, statistical inference, asymptotic theory, generalized least squares, instrumental variables, maximum likelihood, big data, simultaneous equations models. (Main reference: Davidson and Mackinnon, "Econometric Theory and Methods", 2009). Syl
10041c Econometrics I (PhD: BDPEMS/DRS/DIW/BSE)Part I of the course 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 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. (Main reference: Hayashi, "Econometrics", 2000. Hamilton, "Time Series Analysis", 1994 ).
10145511 Multivariate Statistical Methods and Applications (MA Seminar: Economics, Statistics)In this seminar, you work on applied or methodological scientific projects. Topics include multiple testing, highdimensional covariance matrix estimation, multivariate distribution theory and forecasting. You will acquire the necessary knowledge regarding the relevant model classes and enhance your programming skills in either R, Python or Matlab. Syl
Summer term (2023)
10146008-6 Financial Econometrics (MA: Economics, Statistics)This course introduces students to financial statistics. Selected topics include extreme value theory, volatility modeling, high-frequency statistics, large dimensional factor modeling and forecast evaluation. The course requires a solid background in statistics and mathematics and some knowledge of economics and finance. Examples drawn from risk management and portfolio management will highlight the practical relevance of the statistical methods. The main objectives are to give students a background that will enable them to understand and critically appraise applied work on financial issues, and to provide students with some practical experience in working with financial data. pdf
This seminar is intended to give participants exposure to state of the art research in time series econometrics and its applications in empirical finance and macroeconomics. Doctoral students are encouraged to present first research projects in these areas during the seminar.
Winter term (2022/23)
10041c Econometrics I (PhD: BDPEMS/DRS/DIW/BSE)Part I of the course 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 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. (Main reference: Hayashi, "Econometrics", 2000. Hamilton, "Time Series Analysis", 1994 ).
In this seminar, you work on applied or methodological scientific projects. Topics include forecast evaluation, models for high-dimensional high-frequency data, factor models, VARs. You will acquire the necessary knowledge regarding the relevant model classes and enhance your programming skills in either R, Python or Matlab. link
Summer term (2022)
10142608-26 Financial Econometrics (MA: Economics, Statistics)This course introduces students to financial statistics. Selected topics include extreme value theory, volatility modeling, high-frequency statistics, large dimensional factor modeling and forecast evaluation. The course requires a solid background in statistics and mathematics and some knowledge of economics and finance. Examples drawn from risk management and portfolio management will highlight the practical relevance of the statistical methods. The main objectives are to give students a background that will enable them to understand and critically appraise applied work on financial issues, and to provide students with some practical experience in working with financial data.
This seminar is intended to give participants exposure to state of the art research in time series econometrics and its applications in empirical finance and macroeconomics. Doctoral students are encouraged to present first research projects in these areas during the seminar.
Winter term (2021/22)
--Sabbatical--Summer term (2021)
10122601-10 Big Data in Economics (BA: Economics)This online course introduces statistrical methods and applications in the context of Big Data. You will further learn how to write a scientific-paper review. We will focus on topics in time series, multiple testing, shrinkage and dimension reduction. pdf
10051 Topics in Time Series Econometrics (PhD: BDPEMS/DRS/DIW)This seminar is intended to give participants exposure to state of the art research in time series econometrics and its applications in empirical finance and macroeconomics. Doctoral students are encouraged to present first research projects in these areas during the seminar.
Winter term (2020/21)
104007 Econometric Analysis I (MA: Economics, Statistics)This course covers the statistical methods needed to understand empirical economic research and to plan and execute independent research projects. Topics include regressions, statistical inference, asymptotic theory, generalized least squares, instrumental variables, maximum likelihood, big data, simultaneous equations models. (Main reference: Davidson and Mackinnon, "Econometric Theory and Methods", 2009) syl
10041c Econometrics I (PhD: BDPEMS/DRS/DIW/BSE)Part I of the course 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 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. (Main reference: Hayashi, "Econometrics", 2000. Hamilton, "Time Series Analysis", 1994 )
101425 Research Topics in Econometrics (MA Seminar: Economics, Statistics)In this seminar, you work on applied or methodological scientific projects. Topics include regime switching models, structural VARs, Bayesian VARs, models for high-frequency data, multivariate GARCH and factor models. You will acquire the necessary knowledge regarding the relevant model classes and enhance your programming skills in either R, Python or Matlab. link
10142111 Empirical Macroeconomics (PhD: BDPEMS/DRS/DIW/BSE, MA: Economics)This seminar is intended to give participants exposure to state of the art research in empirical macroeconomics. The course provides doctoral students and advanced master students the opportunity to present their own, preliminary research. Program
104100 Univariate Time Series Analysis (Master: Statistics/Economics)
The course provides a survey of the theory and application of univariate time series methods in econometrics. Topics covered will include stationary and non-stationary models, frequency domain methods, models for volatility, structural breaks, time-varying parameters and predictions with many covariates. The empirical applications in the course will be drawn primarily from macroeconomics and finance.
All materials are provided via Blackboard. Please register for the course with your Blackboard account.