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Contents and purpose of the course

The course aims at describing different approaches that allow researchers to sensibly assess the impact of a factor X on an outcome Y when such impact is heterogeneous across individuals and heterogeneity is either observed or unobserved. It will mainly focus on identification and estimation in models that do not assume additive heterogeneity. Features of a particular econometric tool suited for these analyses, namely quantile regression (QR), will be illustrated from a theoretical point of view and through the discussion of empirical applications, with emphasis on the latter. Typical use of the tools explored in this course include wage analysis, returns to schooling, analysis of the determinants of the gender gap, asymmetries in labour supply.

At the end of the course students should be able to understand scientific articles and to perform their own analysis using the tools illustrated.

Topics

1. Dealing with heterogeneity in linear models (review) and non linear models (include QR intro.)

2. Quantile regression with exogenous regressors; software introduction (computer lab.)

3. Quantile regression with exogenous regressors; empirical applications (computer lab.)
(Abrevaya (2001), Martins et al. (2004), Bitler et al. (2006, 2008), Machado et al. (2005))

4. Quantile regression with endogenous regressors (the IV model for quantile treatment effect (QTE) by Chernozhucov et al. (2005); LATE-QTE by Abadie et al. (2002)); empirical applications(Chernozhucov et al. (2004, 2006), Abadie et al. (2002))

5. Quantile regression with endogenous regressors (the causal chain model by Chesher (2003));empirical application (Arias et al. (2001), Ma et al. (2006) )

Software : STATA, R

Readings (preliminary)

1. Abadie, A. et al. (2002) Instrumental Variable Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings, Econometrica, Vol. 70 (1), pp. 91-117.
2. Abrevaya, J. (2001) The Effects of Demographics and Maternal Behaviour on the Distribution of Birth Outcomes, Empirical Economics, Vol. 26, pp. 247-257.
3. Arias, O. et al. (2001) Individual Heterogeneity in the Returns to Schooling: Instrumental Variables Quantile Regression Using Twins Data, Empirical Economics, Vol. 26, pp. 7-40.
4. Bitler, M. et al. (2006) What Mean Impacts Miss: Distributional Effects of Welfare Reform Experiments, American Economic Review, Vol. 96 (4), pp. 988-1012.
5. Buchinsky, M. (1998) Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research, The Journal of Human Resources, Vol. 33 (1), pp. 88-126.
6. Chernozhucov, V. et al. (2004) The Effects of 401(K) Participation on the Wealth Distribution: An Instrumental Quantile Regression Analysis, The Review of Economics and Statistics, Vol. 86 (3), pp. 735-751.
7. Chernozhucov, V. et al. (2005) An IV Model of Quantile Treatment Effects, Econometrica, Vol. 73 (1), pp. 245-261.
8. Chernozhucov, V. et al. (2006) Instrumental Quantile Regression Inference for Structural and Treatment Effect Models, Journal of Econometrics
9. Chesher, A. (2003) Identification in Nonseparable Models, Econometrica, Vol. 71, pp. 1405-1441.
10. Chesher, A. (2005) Nonparametric Identification under discrete variation, Econometrica, Vol. 73 (5), pp. 1525-1550.
11. Koenker, R. (2005) Quantile Regression, Cambridge University Press, Econometric Society Monograph 38.
12. Mata, J. et al. (2005) Counterfactual Decomposition of Changes in Wage Distributions Using Quantile Regression, Journal of Applied Econometrics, Vol. 20 (4), pp. 445-465.
13. Martins et al. (2004) Does Education Reduce Wage Inequality? Quantile Regression Evidence from 16 Countries, Labour Economics, Vol. 11. pp. 355-371.
14. Ma, L. et al. (2006) Quantile Regression Methods for Recursive Structural Equation Models, Journal of Econometrics, Vol. 134 (2), pp. 471-506.

Further references may be made available at the beginning of the course

Teaching Material

Lecture notes, slides, class exercises and solutions, datasets will be made available online before each seminar and distributed at the end of the course, after revision.

Time and place

The course will be held in April-May 2009 and consists of 15 hours (lectures, laboratory sessions).

Assessment

Student presentation (60% of the final score) short test (40% of the final score).