Econometrics II


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.

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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.