Asymptotic distribution-free tests for semiparametric regressions with dependent data
Juan Carlos Escanciano, Juan Carlos Pardo-Fernández, Ingrid Van Keilegom
The Annals of Statistics · 2018 · Volume 46 · Issue 3
Abstract
This article proposes a new general methodology for constructing nonparametric and semiparametric Asymptotically Distribution-Free (ADF) tests for semiparametric hypotheses in regression models for possibly dependent data coming from a strictly stationary process. Classical tests based on the difference between the estimated distributions of the restricted and unrestricted regression errors are not ADF. In this article, we introduce a novel transformation of this difference that leads to ADF tests with well-known critical values. The general methodology is illustrated with applications to testing for parametric models against nonparametric or semiparametric alternatives, and semiparametric constrained mean–variance models. Several Monte Carlo studies and an empirical application show that the finite sample performance of the proposed tests is satisfactory in moderate sample sizes.
Keywords
62E20, 62G08, 62G20, 62H15, Beta-mixing, error distribution, Goodness-of-fit tests, Local polynomial estimation, Nonparametric regression
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