Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data
Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data
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DOI：
10.1017/psrm.2014.7
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发表时间：
2015-01-01
影响因子：
3.9
通讯作者：
Jones, Kelvyn
中科院分区：
文献类型：
--
作者：
Bell, Andrew;Jones, Kelvyn
本文挑战固定效应（FE）建模作为时间序列横截面和面板数据的“默认”。在选择建模策略时，了解效应内部和效应之间的差异至关重要。随机效应（RE）建模的缺点-相关的较低水平的协变量和较高水平的残差-被忽略了变量偏差，可以用Mundlak（1978 a）公式解决。因此，RE可以提供FE所承诺的一切，甚至更多，正如蒙特-卡罗模拟所证实的那样，当数据不平衡时，该模拟还显示了Plumper和Troeger的FE矢量分解方法的问题。除了包含时不变变量外，RE模型还具有随机系数、跨层交互和复杂方差函数，易于扩展。我们认为，不仅是技术解决方案，内隐，但实质性的重要性，上下文/异质性，使用RE建模。其影响超出了政治科学的所有多层次数据集。然而，省略的变量仍然可能使估计的更高水平的变量效应产生偏差;与任何模型一样，在解释时需要小心。
This article challenges Fixed Effects (FE) modeling as the 'default' for time-series-cross-sectional and panel data. Understanding different within and between effects is crucial when choosing modeling strategies. The downside of Random Effects (RE) modeling-correlated lower-level covariates and higher-level residuals-is omitted-variable bias, solvable with Mundlak's (1978a) formulation. Consequently, RE can provide everything that FE promises and more, as confirmed by Monte-Carlo simulations, which additionally show problems with Plumper and Troeger's FE Vector Decomposition method when data are unbalanced. As well as incorporating time-invariant variables, RE models are readily extendable, with random coefficients, cross-level interactions and complex variance functions. We argue not simply for technical solutions to endogeneity, but for the substantive importance of context/heterogeneity, modeled using RE. The implications extend beyond political science to all multilevel datasets. However, omitted variables could still bias estimated higher-level variable effects; as with any model, care is required in interpretation.