Quantile Regression for Time-Series-Cross-Section Data

Abstract

This paper introduces quantile regression methods for the analysis of time-series-cross-section data. Quantile regression offers a robust, and therefore efficient alternative to least squares estimation. We show that quantile regression can be used in the presence of endogenous covariates, and can also account for unobserved individual effects. Moreover, the estimation of these models is no more demanding today than that of a least squares model. We use quantile regression methods to re- examine the hypothesis that higher income leads to democracy, obtaining a series of new insights not available under traditional approaches.