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Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data*

Bell, Andrew; Jones, Kelvyn

Political science research and methods -- Cambridge University Press -- Volume: 3 1; (pages 133-153) -- 2015

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  • Title:
    Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data*
  • Author: Bell, Andrew;
    Jones, Kelvyn
  • Found In: Political science research and methods. Volume 3:Issue 1(2015); 2015; 133-153
  • Journal Title: Political science research and methods
  • Subjects: Political science; LCSH: Political science; Dewey: 320
  • Rights: legaldeposit
  • Publication Details: Cambridge University Press
  • Abstract: <x>Abstract</x>

    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 Plümper 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.


  • Identifier: Journal ISSN: 2049-8470
  • Publication Date: 2015
  • Physical Description: Electronic
  • Shelfmark(s): ELD Digital store
  • UIN: ETOCvdc_100040690234.0x000001

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