Escanciano, Juan Carlos
Pardo-Fernandez, Juan Carlos
Van Keilegom, Ingrid
[UCL]
(eng)
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.
| Bibliographic reference |
Escanciano, Juan Carlos ; Pardo-Fernandez, Juan Carlos ; Van Keilegom, Ingrid. Asymptotic distribution-free tests for semiparametric regressions with dependent data. In: Annals of Statistics, Vol. 46, no. 3, p. 1167-1196 (2018) |
| Permanent URL |
http://hdl.handle.net/2078.1/185665 |
