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Microeconometrics

Objectives

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

Code

2165

Credits

7.0

Responsible teacher

Alex Armand

Hours

Weekly - Available soon

Total - Available soon

Teaching language

English

Prerequisites

n/a

Bibliography

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 (15% of your final grade)

Group assignment (30% of your final grade)

Final exam (55% of the final grade)

Subject matter

The course will cover the following topics. The course outline is a guide to the general nature and order of the topics covered in the course. Some variation may occur.

General microeconometrics approach

Identification in linear models (cross-section data)

Counterfactuals - from ATE to LATE

Identification in linear models (panel data)

Non-linear models and the maximum likelihood

Latent variable models

Censored data

Sample selection

GMM