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arXiv · 2006.06350

Wilks' theorem for semiparametric regressions with weakly dependent data

Marie Du Roy de Chaumaray·Matthieu Marbac·Valentin Patilea
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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.

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Marie Du Roy de Chaumaray, Matthieu Marbac, Valentin Patilea. 2020-06-11. Wilks' theorem for semiparametric regressions with weakly dependent data. https://arxiv.org/abs/2006.06350

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