Weighted residual empirical processes, martingale transformations, and model specification tests for regressions with diverging number of parameters

TL;DR

This paper develops new weighted residual empirical process-based tests for regression models with diverging parameters, balancing power and distribution-free properties, and validates them through simulations and real data.

Contribution

It introduces novel tests that achieve optimal detection rates without distributional assumptions and analyzes their limitations and advantages over martingale-transformed tests.

Findings

Tests without martingale transformations detect local alternatives at rate n^{-1/2}

Martingale-transformed tests are distribution-free but detect alternatives at rate n^{-1/4}

Simulation and real data validate the proposed methods.

Abstract

This paper explores hypothesis testing for the parametric forms of the mean and variance functions in regression models under diverging-dimension settings. To mitigate the curse of dimensionality, we introduce weighted residual empirical process-based tests, both with and without martingale transformations. The asymptotic properties of these tests are derived from the behavior of weighted residual empirical processes and their martingale transformations under the null and alternative hypotheses. The proposed tests without martingale transformations achieve the fastest possible rate of detecting local alternatives, specifically of order $n^{-1/2}$, which is unaffected by dimensionality. However, these tests are not asymptotically distribution-free. To address this limitation, we propose a smooth residual bootstrap approximation and establish its validity in diverging-dimension settings. In contrast, tests incorporating martingale transformations are asymptotically distribution-free but exhibit an unexpected limitation: they can only detect local alternatives converging to the null at a much slower rate of order $n^{-1/4}$, which remains independent of dimensionality. This finding reveals a theoretical advantage in the power of tests based on weighted residual empirical process without martingale transformations over their martingale-transformed counterparts, challenging the conventional wisdom of existing asymptotically distribution-free tests based on martingale transformations. To validate our approach, we conduct simulation studies and apply the proposed tests to a real-world dataset, demonstrating their practical effectiveness.