Course description
This course is ideal for students who intend to do empirical research or are already writing an empirical thesis. We will cover basic issues connected with doing an empirical research: choosing an appropriate model, collecting & understanding the data, performing estimations, interpreting the results and doing robustness checks. The course will be run in the form of seminar-type lectures and practice sessions with students’ presentations and open discussions.

Readings
Moretti E. (2004) Estimating the social return to higher education:evidence from longitudinal and repeated cross-sectional data, Journal of Econometrics 121, pp. 175 – 212 !Download!
Card D. (1993) Using geographic variation in college proximity to estimate the return to schooling, NBER Working Paper No.4483 !Download!
Levitt S.C. (1997) Using electoral cycles in police hiring to estimate the effect of police on crime, Amrican Economic Review 87(3), pp. 279-290 !Download!
Blackburn M. and Neumark D. (1992) Unobserved Ability, Efficiency Wages, and Interindustry Wage Differentials, The Quarterly Journal of Economics 107(4), pp. 1421-1436 !Download!
Ciccone A. and Peri G. (2005) Long-Run Substitutability Between More and Less Educated Workers: Evidence from U.S. States, 1950–1990, eview of Economics and Statistics, 87(4), pp.652-663 !Download!
Bertrand M. and Mullainathan S. (2004) Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination, American Economic Review, 94(4), pp. 991-1013 !Download!

Statistical software
1. Gretl - downloadable from http://gretl.sourceforge.net/
  Gretl User Guide
2. R-project downloadable from http://www.r-project.org/
  R User Guide

Syllabus
This is a tentative schedule to be updated basing on students' progress.
Week 1: Introduction
Lecture 1
Weeks 1-2: Repetition of basic econometrics
(OLS estimation, assumptions, basic properties, hypothesis testing)
Lecture 2
Week 3: Designing an empirical research
(posting a question, formulating an econometric model)
Lecture 3
Week 4: Data
(collection; cleaning the data – outliers, infected observations; preliminary analysis – means, standard deviations, correlations; graphs)
Lecture 4
Weeks 5-8: Identification strategy - how can we make the data answer our question
(choosing the control variables; causality – what influences what; comparing similar individuals/firms/countries; understanding and dealing with endogeneity)
Lecture 5
Lecture 6
Lecture 7
Lecture 8
Weeks 8-10: Practical examples of econometric analysis - discussing students' research and selected scientific publications
Week 11: Interpretation and presentation of results
Week 12: Repetition