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arXiv · 2412.20555

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

Andrew T. Karl
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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.

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Andrew T. Karl. 2024-12-29. Parameter-Specific Bias Diagnostics in Random-Effects Panel Data Models. https://arxiv.org/abs/2412.20555

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