Testing for Structural Change in Regression Quantiles

Abstract

Most studies in the structural change literature focus solely on the conditional mean, while under various circumstances structural change in the conditional distribution or in conditional quantiles is of key importance. This paper proposes several tests for structural change in regression quantiles. Two types of statistics are considered, namely, a fluctuation type statistic based on the subgradient and a Wald type statistic based on comparing parameter estimates obtained from different subsamples. The former requires estimating the model under the null hypothesis and the latter involves estimation under the alternative hypothesis. The tests proposed can be used to test for structural change occurring in a pre-specified quantile, or across quantiles, which can be viewed as testing for change in the conditional distribution with a linear specification of the conditional quantile function. Both single and multiple structural changes are considered. We derive the limiting distributions under the null hypothesis and show they are nuisance parameter free and can be easily simulated. A simulation study is conducted to assess the size and power in finite samples.