Specification tests for regression models with measurement errors

TL;DR

This paper introduces new specification tests for regression models with measurement errors, utilizing a deconvolution approach and a novel multiplier bootstrap method for improved accuracy and applicability.

Contribution

It develops the first multiplier bootstrap procedure for measurement error models and extends tests to unknown error distributions, enhancing testing robustness.

Findings

Tests perform well in finite samples according to simulations.

The multiplier bootstrap effectively approximates critical values.

Proposed methods are applicable even with unknown measurement error distributions.

Abstract

In this paper, we propose new specification tests for regression models with measurement errors in the explanatory variables. Inspired by the integrated conditional moment (ICM) approach, we use a deconvoluted residual-marked empirical process and construct ICM-type test statistics based on it. The issue of measurement errors is addressed by applying a deconvolution kernel estimator in constructing the residuals. We demonstrate that employing an orthogonal projection onto the tangent space of nuisance parameters not only eliminates the parameter estimation effect but also facilitates the simulation of critical values via a computationally simple multiplier bootstrap procedure. It is the first time a multiplier bootstrap has been proposed in the literature of specification testing with measurement errors. We also develop specification tests and the multiplier bootstrap procedure when the measurement error distribution is unknown. The finite-sample performance of the proposed tests for both known and unknown measurement error distributions is evaluated through Monte Carlo simulations, which demonstrate their efficacy.