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.

Instructor:
Description:

The lecture deals with the statistical properties of financial market data and econometric methods that can be used to analyze these data. We will study procedures to test for the efficient market hypothesis and become familiar with methods to model the mean and the volatility of financial data series. Besides the application of nonparametric and classical test procedures, the focus will be on time series methods and models. In particular, ARMA and GARCH models will be covered.

Credits:
9.00
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Instructor:
Description:

This is a methodological course on statistics with a focus on research techniques used in management science. The pre-requisites are a basic level of statistics and probability theory, including distributions, sampling and inference. The topics covered in this course include linear and nonlinear modeling, time-to-event analysis and factor analysis. Applications will focus on areas such as pricing and revenue management. The course will also include empirical work, underlining the link between theory and applications and using statistical software packages such as SPSS, Matlab and SAS. The techniques developed in this course are fundamental research tools in modern management science, and they enable the students to engage in research at the interface of fields such as operations, marketing, finance, and strategy.

Literature:
Details will be provided soon.

Credits:
4.50
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Instructor:
Description:

The aim of microeconometrics is to analyze individual behavior on the basis of micro data (crosssection and panel data) of individuals, households, and firms. The standard linear regression model is generally not applicable to micro data due to the non-metric measurement and censoring of dependent variables at the individual level, selectivity and incomplete observability of endogenous variables, and the dependence of individual observations over time. The empirical methods most frequently applied in empirical microeconomics are surveyed and several applications in empirical microeconomics are presented. Students learn how to apply these methods using real-world microdata and the software package STATA.

Credits:
6.00
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Instructor:
Description:

Participating students are expected to be familiar with basic time series analysis and methods of econometrics. The course covers advanced methods of modelling and analysing multiple time series. Students are introduced to the models, parameter estimation and specification of the relevant models. They will learn to use them for economic analysis and forecasting. 

Contents:

  • Review of univariate time series analysis
  • Vector autoregressive (VAR) models
  • Specification and estimation of VAR models
  • Cointegration
  • Vector error correction models (VECMs)
  • Estimation of VECMs
  • Cointegration tests and specifications of VECMs
  • Structural vector autoregressive analysis

Literature: 
H. Lütkepohl, New Introduction to Multiple Time Series Analysis, Springer, Berlin, 2005.

Credits:
6.00
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Instructor:
Description:

Most of the observable phenomena in the empirical sciences are of a multivariate nature. In financial studies, assets in the stock markets are observed simultaneously and their joint development is analyzed to better understand general tendencies and to track indices. In medicine recorded observations of subjects in different locations are the basis of reliable diagnoses and medication. In quantitative marketing consumer preferences are collected in order to construct models of consumer behavior. The underlying theoretical structure of these and many other quantitative studies of applied sciences is multivariate. The course of Multivariate Statistical Analysis (MVA) describes a collection of procedures which involve observation and analysis of more than one statistical variable at a time.

Literature:
Härdle, Simar (2007, 2nd extended ed.). Applied Multivariate Statistical Analysis, Springer Verlag

Credits:
9.00
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Instructor:
Description:

The course Non- and Semiparametric Modelling gives an overview over the flexible regression methods. The course starts with an introduction into the density estimation (histogram, kernel density estimation). Nonparametric regression methods and their applications are discussed. Furthermore additive models will be introduced in the course. At the end of the course the students will be able to implement methods to solve practical problems.

Literature:
Härdle, Müller, Sperlich, Werwatz (2004): Non- and Semiparametric Modelling, Springer
Fan, J. and Gijbels, I. (1996): Local Polynomial Modelling and Its Applications, Chapman and Hall, New York
Härdle, W. (1990): Applied Nonparametric Regression, Econometric Society Monographs No. 19, Cambridge University Press
Härdle, W. (1991): Smoothing Techniques, With Implementations in S, Springer, New York
Härdle, Klinke, Müller (1999): XploRe - Academic Edition, The Interactive Statistical Computing Environment, Springer, New York
Scott, D. W. (1992): Multivariate Density Estimation: Theory, Practice, and Visualization, John Wiley & Sons, New York, Chichester
Silverman, B. W. (1986): Density Estimation for Statistics and Data Analysis, Vol. 26 of Monographs on Statistics and Applied Probability, Chapman and Hall, London
Wand, M. P. and Jones, M. C. (1995): Kernel Smoothing, Chapman and Hall, London
Yatchew, A., (2003): Semiparametric Regression for Applied Econometrician, Cambridge University Press, Cambridge

Credits:
6.00
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Instructor:
Description:

The course aims at providing the basic concepts and methods for analyzing panel data. It begins with introducing different static panel models with fixed and random effects, and discusses the problem of estimation in these models. The course covers tests of hypotheses with panel data as well as techniques for serial correlation, heteroscedasticity, simultaneous equations, dynamic models and models for qualitative dependent variables.

In the tutorials the methods are revisited and applied to empirical data using the software STATA. A deeper insight into advanced methods and additional topics is offered by means of assignments, empirical studies and/or literature reviews.

Literature:
- Baltagi, B.H., (2005), Econometric Analysis of Panel Data, 3rd ed., Wiley, New York.
- Hsiao, C., (2003), Analysis of Panel Data, 2nd ed., Cambridge University Press.
- Arellano, M. (2003), Panel Data Econometrics, Oxford: Oxford University Press.

Credits:
9.00
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Instructor:
Description:

The course covers a part of mathematical statistics which deals with the limiting behavior of different sample statistics, U-statistics, M-, L- and R-Estimates. This course gives better understanding for the basic tools learned in the elementary Statistics I and II, like Law of Large Numbers, Central Limit Theorem, Kolmogorov-Smirnov and Cramer-von-Mises tests, sample mean and sample variance behavior, etc. This course is laying a bridge between the probability theory and the mathematical statistics by manipulating with "probability" theorems to obtain "statistical" theorems. In the first part of the course we discuss basic tools of asymptotic theory in statistics: convergence in distribution, in probability, almost surely, in mean. We also consider main probability limit laws: LLN and CLT. Then we deal with the usual statistics computed from a sample: the sample distribution function, the sample moments, the sample quantiles, the order statistics. Properties, such as asymptotic normality and almost sure convergence will be derived in the lecture. Afterwards, comes the asymptotics of statistics concocted as transformations of vector of more basic statistics. Next part concerns statistics arising in classical parametric inference and contingency table analysis. These include maximum-likelihood estimates, likelihood-ratio tests, etc. Last part of the course treats U-statistics, statistics obtained as solutions of equations (M-estimates), linear function of order statistics (L-estimates) and rank statistics (R-estimates).

Literature:
Sering R.J. (1980) Approximation Theorems of Mathematical Statistics / Wiley.

Credits:
6.00
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Instructor:
Description:

The course offers an overview of advanced statistical methods in quantitative finance and insurance which should be comprehensible for a graduate student in financial engineering as well as for an inexperienced newcomer who wants to get a grip on advanced statistical tools applied in these fields.

Credits:
6.00
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Instructor:
Description:

The course starts with an introduction into the basic concepts of option pricing and its probabilistic foundations. Next, stochastic processes in discrete time are presented and the Wiener process is introduced. Itô's Lemma is derived and the Black-Scholes (BS) Option model is presented leading to the analytic solution for the BS Option price. Numerical solutions via a binomial or trinomial tree constructions are discussed in detail.
Literature:

Franke, J., Härdle, W., and Hafner, C. (2011) Statistics of Financial Markets: an Introduction. 3rd ed., Springer Verlag, Heidelberg. ISBN: 978-3-642-16520-7 (599 p)
Härdle, W., Hautsch, N. and Overbeck, L. (2009) Applied Quantitative Finance. 2nd extended ed., Springer Verlag, Heidelberg. ISBN 978-3-540-69177-8 (448 p)
Hull (2005) Options, Futures, and Other Derivatives. 6th ed., Prentice Hall. ISBN 0-13-149908-4 (816 p)
Härdle, W., Simar, L. (2007) Applied Multivariate Statistical Analysis. 2nd extended ed., Springer Verlag, Heidelberg. ISBN 3-540-72243-4 (456 p)
Cizek, P., Härdle, W., Weron, R. (2011) Statistical Tools for Finance and Insurance. 2nd ed., Springer Verlag, Heidelberg. ISBN: 978-3-642-18061-3 (420 p)

Credits:
9.00
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Instructor:
Description:

Please note: This course is held in German and it is organized by the Department of Mathematics, Humboldt-Universität zu Berlin.
Einführung in die zur Analyse zufälliger Erscheinungen entwickelten mathematischen Ideen und Methoden, Gesetze der großen Zahlen und zentrale Grenzwertsätze, Elemente der Mathematischen Statistik.

Literature:
Georgii, H.-O. (2007). Stochastik, De Gruyter Verlag
Klenke, A. (2008). Wahrscheinlichkeitstheorie, Springer
Elstrodt, J. (2007). Maß- und Integrationstheorie, Springer

Credits:
9.00
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Instructor:
Description:

Estimating a causal effect or "treatment effect" from nonexperminatal data is the aim of much empirical research in economics. This course will cover the most important concepts and methods in this field from an applied perspective. The proposed schedule is (i) Rubin Model of Causality, (ii) Roy Model of Self-Selection, (iii) Causality and Regression Notation, (iv) Experiments, (v) Conditional Independence, (vi) Heckman Switching Regression, (vii) Instrumental Variables and Local Average Treatment Effect, (viii) Difference-in-Differences and Panel Methods, (ix) Regression Discontinuity Design.

The tutorials provide the opportunity to apply the methods covered in class to real data using the software STATA.

The course grade will be primarily based on the final exam. BDPEMS students also need to submit a referee report on a paper that attempts to estimate a causal effect by applying one of the methods presented in class. Many suitable papers can be found on the current and past seminar schedule at http://www.arbeitsmarktforschung.net/.

Literature: Mostly Harmless Econometrics by Angrist and Pischke

Credits:
6.00
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