Wilks' theorem for semiparametric regressions with weakly dependent data
Methodology
2021-05-18 v2
Abstract
The empirical likelihood inference is extended to a class of semiparametric models for stationary, weakly dependent series. A partially linear single-index regression is used for the conditional mean of the series given its past, and the present and past values of a vector of covariates. A parametric model for the conditional variance of the series is added to capture further nonlinear effects. We propose a fixed number of suitable moment equations which characterize the mean and variance model. We derive an empirical log-likelihood ratio which includes nonparametric estimators of several functions, and we show that this ratio has the same limit as in the case where these functions are known.
Cite
@article{arxiv.2006.06350,
title = {Wilks' theorem for semiparametric regressions with weakly dependent data},
author = {Marie Du Roy de Chaumaray and Matthieu Marbac and Valentin Patilea},
journal= {arXiv preprint arXiv:2006.06350},
year = {2021}
}