Parameter-Specific Bias Diagnostics in Random-Effects Panel Data Models

TL;DR

This paper introduces a parameter-specific bias diagnostic for random-effects panel data models that complements the Hausman test by providing finite-sample bias estimates and p-values for individual parameters.

Contribution

It presents a new bias diagnostic method that offers parameter-specific bias estimates and significance testing within a single random-effects model, enhancing model assessment.

Findings

The diagnostic provides finite-sample bias estimates for parameters.

Permutation-based p-values assess the significance of bias.

Application examples demonstrate practical use in panel data analysis.

Abstract

The Hausman specification test assesses the random-effects specification by comparing the random-effects estimator with a fixed-effects alternative. This note shows how a recently proposed bias diagnostic for linear mixed models can complement that test in random-effects panel-data applications. The diagnostic delivers parameter-specific internal estimates of finite-sample bias, together with permutation-based $p$-values, from a single fitted random-effects model. We illustrate its use in a gasoline-demand panel and in a value-added model for teacher evaluation using publicly available \textsf{R} packages, and we discuss how the resulting coefficient-specific bias summaries can be incorporated into routine practice.