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

The Hausman specification test detects inconsistency of the random-effects estimator by comparing it with an alternative fixed-effects estimator. This note shows how a recently proposed bias diagnostic for linear mixed models can complement this test in random-effects panel-data applications. The diagnostic delivers parameter-specific internal estimates of finite-sample bias of the random-effects estimator, 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 R packages, and we discuss how the resulting bias summaries can be incorporated into routine practice.