The course covers a range of advanced econometric techniques frequently employed in the analysis of micro data (at the household, individual or firm level). The course intends to make the student familiar with basic statistical and econometric techniques that are used in the analysis of microeconomic data. Lectures combine theory and empirical applications and will include examples and discussions of empirical papers that employ the different techniques. 

General characterization





Responsible teacher

Alex Armand


Weekly - Available soon

Total - Available soon

Teaching language





The course will not follow a specific textbook, but the following three books are good general references for microeconometrics. Additional readings to the textbook will be posted in the course Moodle webpage.

Wooldridge, J. (2001), Econometric Analysis of Cross-Section and Panel Data. MIT Press, Cambridge, MA.

Angrist, Joshua D., and Steffen Pischke (2008), Mostly harmless econometrics: An empiricists companion. Princeton university press, 2008.

[MORE ADVANCED] Cameron, A. C., and P. K. Trivedi (2005), Microeconometrics: Methods and Applications. Cambridge University Press, New York, NY 

Teaching method

A variety of teaching and learning methods will be used in this course: lectures covering both theoretical and empirical topics, weekly problem sets with applications of the material covered during lectures, and a group assignment with an econometric project to be carried out 

Evaluation method

The assessment of the course is composed of the following components: Three problem sets (20% of your final grade) Group assignment (30% of your final grade) Final exam (50% of the final grade). In accordance with the school norms, there is no procedure for grade improvement after passing a course (no re-sit or second course enrolment). 

Subject matter

Identification and linear models. Violation of orthogonality and instrumental variables. Identification in repeated cross-section and panel data. The selection problem and solutions to identification. Non-linear models and the maximum likelihood. Latent variable models. Censored data and sample selection. Introduction to non-parametric econometrics