The purpose of this course is to review and discuss a number of econometric and statistical techniques that are essential for empirical research in economics.

The course would be devoted to techniques that are mainly used in microeconomic studies, labour economics, technology, industrial Organization. The emphasis will be on the understanding of the fundamentals behind the techniques used, their applicability, empirical relevance, economic interpretation, their limitations, both from an empirical and methodological point of view . Each topic will be illustrated by empirically papers published in a leading economic journal illustrating the use of the techniques.

The students will work on empirical paper(s)/project(s) to learn the applications of the techniques and models discussed. The econometrics/statistical package that will be mainly used through the course is STATA.



List of possible topics that will be discussed during the course:

* Causal models, OLS, IV
* Binary outcome models (logit, probit …)
* Unobserved heterogeneity
* Multinomial models
* Tobit and selection Models
* Treatment Effect causal models, policy evaluation, regression discontinuity, ...
* Survival analysis and transition analysis
* GMM estimation of intertemporal models in microeconomics
* Count data models, poison regression models

Cameron, A.C. and P. K. Trivedi (2005), Microeconometrics: Methods and Applications, (Cambridge University Press, Cambridge) .
Angrist, J.A. and J.S. Pischke (2009), Mostly Harmless Econometrics, (Princeton University Press, Princeton).
Greene, W.H. (2007) Econometric Analysis, (Prentice Hall, New York).
Wooldridge, Jeffrey M. (2011), Econometric Analysis of Cross Section and Panel Data, 2nd edition (MIT Press, Cambridge).
Empirical papers from leading economic journals.

We assume that the students entering the Research master and following this course have at least a level comparable to the IES bachelor course Empirical Econometrics; have a good working knowledge of matrix algebra, of integrals calculus and are familiar with concepts from probability theory and mathematical statistics.