Journal
POLITICAL SCIENCE RESEARCH AND METHODS
Volume 3, Issue 1, Pages 133-153Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.1017/psrm.2014.7
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Funding
- ESRC [ES/I025065/1] Funding Source: UKRI
- Economic and Social Research Council [ES/I025065/1, 1099939] Funding Source: researchfish
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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.
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