Estimating Structural Changes in Regression Quantiles

Abstract

This paper considers the estimation of multiple structural changes occurring at unknown dates in one or multiple conditional quantile functions. The analysis covers time series models as well as models with repeated cross sections. We estimate the break dates and other parameters jointly by minimizing the check function over all permissible break dates. The limiting distribution of the estimator is derived and the coverage property of the resulting confidence interval is assessed via simulations. A procedure to determine the number of breaks is also discussed. Empirical applications to the quarterly US real GDP growth rate and the under-age drunk driving data suggest that the method can deliver more informative results than the analysis of the conditional mean function alone.

Keywords: structural change, quantile regression, conditional distribution