 Testing the parametric form of the conditional variance in regressions based on distance covarianceYue Hu, Haiqi Li and Falong Tan Computational Statistics & Data Analysis, 2024, vol. 189, issue C Abstract: A new test based on distance covariance is proposed for testing the parametric form of the conditional variance in parametric and nonparametric regression models. Inherit from the nice properties of distance covariance, the new test is very easy to implement in practice and less effected by the dimensionality of covariates. The asymptotic properties of the test statistic are investigated under the null and alternative hypotheses. The proposed test is consistent against any alternative hypothesis and can detect some classes of local alternative hypotheses converging to the null hypothesis at the parametric rate in both the parametric and nonparametric settings. As the limiting null distribution of the test statistic is intractable, a smooth residual bootstrap is proposed to approximate the limiting null distribution. Simulation studies are conducted to assess the finite sample performance of the proposed test. A real data set is also analyzed for illustration. Keywords: Distance covariance; Heteroscedasticity; Nonparametric regression models; Smooth residual bootstrap (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:189:y:2024:i:c:s0167947323001627 DOI: 10.1016/j.csda.2023.107851 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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