Asymptotic distribution-free tests for semiparametric regressions with dependent data Articles uri icon

publication date

  • June 2018

start page

  • 1167

end page

  • 1196


  • 3


  • 43

International Standard Serial Number (ISSN)

  • 0090-5364

Electronic International Standard Serial Number (EISSN)

  • 0003-4851


  • 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.


  • Economics


  • beta-mixing, error distribution; goodness-of-fit tests; local polynomial estimation; nonparametric regression