Asymptotic distribution-free tests for semiparametric regression models
Juan Carlos Pardo Fernandez
FCEyN-UBA

In this seminar, we will present 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. Here, 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 show that the finite sample performance of the proposed tests is satisfactory in moderate sample sizes.

This is joint work with Juan Carlos Escanciano (Indiana University, USA) and Ingrid Van Keilegom (KU Leuven, Belgium).