Topics in Advanced Econometrics


Modern empirical research in economics and other social sciences are confronted with a vast menu of modeling strategies resulting from alternative data sets, huge numbers of covariates potentially useful for the target of investigation and an increasing number of available estimation approaches. The overflow of these data, variables, and estimators makes it difficult to select the best modeling technique. The problem is prevalent in macroeconomic forecasting, financial econometrics (e.g. portfolio management), evaluation econometrics and in general in any empirical study where the number of possible modeling strategies prohibits conventional hypothesis testing approaches.

The goal of the PhD/Master level course is to provide an introduction to several topics in frontier econometric and statistical modeling strategies which are beyond the conventional canon of parametric approaches based on frequentist inference and asymptotic theory.

Very often the theoretically best estimation approaches in terms of their asymptotic properties are far from being optimal if other criteria (e.g. finite sample properties, forecasting performance) are taken into account. Data quality is quite often a crucial factor. Students will be acquainted with new ideas available in the literature, some of which differ strongly from the standard framework of consistent, asymptotically normal estimators and from conventional approaches in general.


Introductory Literature

Bühlmann, P., S. van der Geer (2011): Statistics for High-Dimensional Data: Methods, Theory and Application, Springer, Heidelberg et al.

Du, K.-L., Swamy, M. N. S. (2014): Neural Networks and Statistical Learning, Springer

Franses, P. H., van Dijk, D. (2003): Nonlinear Time Series Models in Empirical Finance, Cambridge University Press

Greenberg, E. (2008): Introduction to Bayesian Econometrics, Cambridge University Press.

Hastie, T., Tibshirani, R., and Friedman, J. (2009): The Elements of Statistical Learning, Springer-Verlag

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013): An Introduction to Statistical Learning, Springer New York

Lancaster, T. (2004): An introduction to modern Bayesian econometrics, Blackwell Publishing.

Li, Q. and J. S. Racine (2006): Nonparametric Econometrics: Theory and Practice. Princeton University Press.

Mittelhammer, R.C., G.G. Judge, and D.J. Miller (2000): Econometric Foundations, Cambridge University Press.

Yatchev, A.(2003): Semiparametric Regression For the Applied Econometrician, Cambridge University Press.

Form of Assessment

8 ECTS: two paper presentations (40%) and an oral examination (60%).

10 ECTS (PhD and M.Sc. Economics fast track): In addition to the 8 ECTS requirements students have to write a research paper (24% / 36% / 40%).

Lecture & Tutorial Dates

Lecture and Tutorial Dates

Date Room Time Lecture/Tutorial Lecturer
23.10.2017 Y311 13:30-15:00 Lecture 1 L. Grigoryeva
30.10.2017 Y311 13:30-15:00 Lecture 2 L. Grigoryeva
06.11.2017 Y311 13:30-15:00 Lecture 3 L. Grigoryeva
07.11.2017 TBA  17:00-18:30 Tutorial 1 L. Grigoryeva
13.11.2017 Y311 13:30-15:00 Lecture 4 L. Grigoryeva
20.11.2017 Y311 13:30-15:00 Lecture 5 L. Grigoryeva
21.11.2017 TBA 17:00-18:30 Tutorial 2 L. Grigoryeva
27.11.2017 Y311 13:30-15:00 Lecture 6 L. Grigoryeva
04.12.2017 Y311 13:30-15:00 Lecture 7 L. Grigoryeva
05.12.2017 TBA  17:00-18:30 Tutorial 3 L. Grigoryeva
11.12.2017 Y311 13:30-15:00 Lecture 8 L. Grigoryeva
12.12.2017 TBA 17:00-18:30 Tutorial 4 L. Grigoryeva
08.01.2018 Y311 13:30-15:00 Lecture 9 L. Grigoryeva
15.01.2018 Y311 13:30-15:00 Lecture 10 L. Grigoryeva
16.01.2018 TBA  17:00-18:30 Tutorial 5 L. Grigoryeva
22.01.2018 Y311 13:30-15:00 Lecture 11 L. Grigoryeva
23.01.2018 D433 17:00-18:30 Lecture 12 G. Calzolari
29.01.2018 Y311 13:30-15:00 Lecture 13 G. Calzolari
30.01.2018 D433 17:00-18:30 Lecture 14 G. Calzolari
05.02.2018 Y311 13:30-15:00 Lecture 15 G. Calzolari
06.02.2018 D433 17:00-18:30 Lecture 16 G. Calzolari

Course Materials

The course material will be provided via ILIAS.