Making Theoretically Informed Choices in Specifying Panel‐Data Models
指出研究者过度依赖统计检验（如Hausman检验）来选择面板数据模型，忽略了检验假设（如组内与组间参数相等）可能不成立，并提出五个问题引导基于理论而非统计的模型设定，对管理学研究有用。
We argue that in analyzing panel‐data econometric models, researchers rely excessively on statistical criteria to determine model specification, treating it primarily as a matter of statistical inference. This inferential emphasis is most obvious in the common practice of using statistical tests (e.g., the Hausman test) to choose between fixed‐ and random‐effects specifications, often ignoring the assumptions underpinning these tests. For instance, the Hausman test depends on the true within‐panel (longitudinal) and between‐panel (cross‐sectional) parameters being equal. This assumption is often not justified, because longitudinal and cross‐sectional variances and covariances may manifest different underpinning mechanisms. In addition to different mechanisms often resulting in different variables determining within and between effects, within and between variables may also have different meanings. To help researchers make theoretically informed choices, we formulate five questions that can guide researchers to think of model specification in a theoretically rigorous way. We examine these issues with examples from both general management and operations management research. Importantly, we argue that addressing the questions regarding model specification must involve primarily theoretical and contextual judgment, not statistical tests.