RT Journal Article
T1 Asymptotic distribution-free tests for semiparametric regressions with dependent data
A1 Escanciano, Juan Carlos
A1 Pardo-Fernandez, Juan Carlos
A1 Van Keilegom, Ingrid
AB 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.
PB Institute of Mathematical Statistics
SN 0090-5364
YR 2018
FD 2018-06-01
LK https://hdl.handle.net/10016/35090
UL https://hdl.handle.net/10016/35090
LA eng
NO Supported by the Spanish Plan Nacional de I+D+I, reference number ECO2014-55858-P.Supported by the SpanishMinisterio de Economía y Competitividad (GrantMTM2014-55966 P).Supported by the European Research Council (2016-2021, Horizon 2020 ERC Grant agreement Nº. 694409), and from IAP Research Network P7/06 of the Belgian State
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RD 1 sept. 2024