Empirical likelihood based testing for regression

TL;DR

This paper develops a new empirical likelihood-based test for various regression models, including parametric, generalized linear, and partial linear models, with proven asymptotic validity and demonstrated finite sample effectiveness.

Contribution

It introduces a novel testing procedure combining empirical likelihood with marked empirical processes for flexible regression hypothesis testing.

Findings

Asymptotic validity of the proposed test is established.

Finite sample performance compares favorably with existing tests.

Applicable to a wide range of regression models.

Abstract

Consider a random vector $(X,Y)$ and let $m(x)=E(Y|X=x)$. We are interested in testing $H_0:m\in {\cal M}_{\Theta,{\cal G}}=\{\gamma(\cdot,\theta,g):\theta \in \Theta,g\in {\cal G}\}$ for some known function $\gamma$, some compact set $\Theta \subset $IR$^p$ and some function set ${\cal G}$ of real valued functions. Specific examples of this general hypothesis include testing for a parametric regression model, a generalized linear model, a partial linear model, a single index model, but also the selection of explanatory variables can be considered as a special case of this hypothesis. To test this null hypothesis, we make use of the so-called marked empirical process introduced by \citeD and studied by \citeSt for the particular case of parametric regression, in combination with the modern technique of empirical likelihood theory in order to obtain a powerful testing procedure. The asymptotic validity of the proposed test is established, and its finite sample performance is compared with other existing tests by means of a simulation study.