Econometrics II

Instructor: 
Guest Instructor: 
Andrija Mihoci (Humboldt-University)
Time I: 
Monday,
02:00pm to 04:00pm
Time II: 
Thursday,
12:00pm to 02:00pm
Venue I: 
HU Berlin, SPA1, Room 203
Venue II: 
HU Berlin, SPA1, Room 22
Description: 

This course deals with advanced estimation techniques in modern econometrics. In the first part we study generalized methods of moments (GMM) estimation as well as pseudo-maximum likelihood techniques and their applications to different types of single-equation models and multiple-equation systems. If time, a brief introduction to Bayesian econometric methods will be given. The second 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, microeconometrics, panel econometrics as well as financial econometrics.

Literature:
Davidson, R. and MacKinnon, J.G. (2004): Econometric Theory and Methods. Oxford University Press.
Gouriéroux, C. and Monfort, A. (1995): Statistics and Econometric Models. Cambridge University Press, Vol. 1 and 2.
Härdle, W.K., Müller, M., Sperlich, S. and Werwatz, A. (2004): Nonparametric and Semiparametric Models. Springer-Verlag.
Hayashi, F. (2000): 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.

Exam: written exam (90 min), two exam dates

Credits: 
9.00
Program: 
Semester: 
Spring 2013
Affiliation: 
Humboldt-Universität zu Berlin
End date of the whole course: 
Wednesday, July 17, 2013 - 2:00pm