Quantile Regression Estimation of a Model with Interactive Effects∗

This paper proposes a quantile regression estimator for a panel data model with interactive effects potentially correlated with the independent variables. We provide conditions under which the slope parameter estimator is asymptotically Gaussian. Monte Carlo studies are carried out to investigate the finite sample performance of the proposed method in comparison with other candidate methods. The paper presents an empirical application of the method to study the effect of class size and class composition on educational attainment. The findings suggest that (i) a change in the gender composition of a class impacts differently lowand high-performing students; (ii) while smaller classes are beneficial for low performers, larger classes are beneficial for high performers; (iii) reductions in class size do not seem to impact mean and median student performance. JEL: C23, C33