Testing for correlation in error‐component models
误差成分模型中的相关性检验AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究含组别效应的分组模型、非平衡数据和组内相关性，并应用于同一母亲的多次生育观测，直接对应微观面板方法。
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误差成分模型中的相关性检验
- 研究问题
- 在含组别效应的线性分组数据模型中，如何检验组内是否存在超出组别效应所解释的相关性，并兼容异方差和短组数据？
- 方法与思路
- 作者构造组内相关性的综合检验，允许异方差及外生、前定或内生解释变量，并在组数增加、每组规模固定的渐近框架下分析性质。
- 材料陈述的贡献
- 摘要称，检验适用于非平衡数据且每组仅需三次观测，并给出检验水平与功效理论；模拟比较及多胎母亲数据展示其表现。
- 阅读关注点
- 核对不同解释变量条件下所需的矩条件、异方差下检验的校准方式，并查看模拟比较是否在可比的原假设和设定下进行。
- 阅读边界
- 摘要未说明模拟参数与各类解释变量所需限制；所述功效优势及母亲固定效应的控制边界，仍需结合全文设定判断。
原文摘要
Summary This paper concerns linear models for grouped data with group‐specific effects. We construct a portmanteau test for the null of no within‐group correlation beyond that induced by the group‐specific effect. The approach allows for heteroskedasticity and is applicable to models with exogenous, predetermined, or endogenous regressors. The test can be implemented as soon as three observations per group are available and is applicable to unbalanced data. A test with such general applicability is not available elsewhere. We provide theoretical results on size and power under asymptotics where the number of groups grows but their size is held fixed. Extensive power comparisons with other tests available in the literature for special cases of our setup reveal that our test compares favorably. In a simulation study we find that, under heteroskedasticity, only our procedure yields a test that is both size correct and powerful. In a large data set on mothers with multiple births we find that infant birthweight is correlated across children even after controlling for mother fixed effects and a variety of prenatal care factors. This suggests that such a strategy may be inadequate to take care of all confounding factors that correlate with the mother's decision to engage in activities that are detrimental to the infant's health, such as smoking.