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A test for homoskedasticity in density-weighted quantile regression
Abstract
In this paper a method is presented to test for heteroskedasticity in quantile regression models. The unknown parameters are estimate using an iteratively weighted procedure with weights derived from the sparsity function of the response variable. The test statistic is
based on Gini concentration index of the weights and its asymptotic distribution is derived under the null hypothesis of homoskedasticity. Unlike most methods in the literature, no parametric form of the skedastic function is required for the asymptotic Gaussianity of the
test. Results are given which support the contention that the test would be a useful aid in achieving early recognition of the inconstancy of the error variance.
Keywords
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