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.

翻译：Hausman 设定检验通过比较随机效应估计量与固定效应替代方案来评估随机效应设定。本说明展示了近期提出的线性混合模型偏差诊断方法如何能在随机效应面板数据应用中补充该检验。该诊断可从单一拟合的随机效应模型中，提供有限样本偏差的参数特异性内部估计以及基于置换的$p$值。我们通过公开可用的\textsf{R}软件包，在汽油需求面板和教师评价增值模型中演示其应用，并讨论如何将所得系数特异性偏差汇总纳入常规实践。