This course deals with advanced estimation techniques in modern econometrics. In the first part we study Pesudo-ML and GMM as extremum estimation problems with special attention to asymptotic theory and the weak instruments problem. The second part covers non- and semi-parametric topics including the bootstrap, density estimation, and non- and semi-parametric regression. The third part covers the concept of econometric identification, and possible frameworks to write down and interpret causal estimands (treatment effects). We also discuss a number of techniques for estimation of treatment effects (IV, Diff-and-Diff, RDD, Matching).
Wooldridge, J. M. (2010): Econometric Analysis of Cross Section and Panel Data. 2nd edition, Cambridge, MA: MIT Press (see also: http://mitpress.mit.edu/books/econometric-analysis-cross-section-and-panel-data ).
Angrist, J. and Pischke, J-S (2009): Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.
Further reading recommendations you will get during the lecture.
StO/PO MA 2016: 6 LP, Modul: "Advanced Econometrics"
StO/PO MEMS 2016: 6 LP, Modul: "Advanced Econometrics", Major: Quantitative Methods
Written exam (90 min)
Die Veranstaltung wurde 7 mal im Vorlesungsverzeichnis SoSe 2025 gefunden:
