A robust specification test in linear panel data models

TL;DR

This paper introduces a new robust specification test for linear panel data models that effectively handles outliers, maintaining reliable inference and improving test performance in contaminated data scenarios.

Contribution

A novel weighted likelihood based robust test for panel data models that preserves asymptotic properties and enhances finite sample robustness against outliers.

Findings

The proposed test maintains the same asymptotic distribution as Hausman's test under the null hypothesis.

Monte Carlo simulations show improved size and power in contaminated data.

Application to OECD economic-growth data demonstrates practical effectiveness.

Abstract

The presence of outlying observations may adversely affect statistical testing procedures that result in unstable test statistics and unreliable inferences depending on the distortion in parameter estimates. In spite of the fact that the adverse effects of outliers in panel data models, there are only a few robust testing procedures available for model specification. In this paper, a new weighted likelihood based robust specification test is proposed to determine the appropriate approach in panel data including individual-specific components. The proposed test has been shown to have the same asymptotic distribution as that of most commonly used Hausman's specification test under null hypothesis of random effects specification. The finite sample properties of the robust testing procedure are illustrated by means of Monte Carlo simulations and an economic-growth data from the member countries of the Organisation for Economic Co-operation and Development. Our records reveal that the robust specification test exhibit improved performance in terms of size and power of the test in the presence of contamination.