articlePolitical Science Research and MethodsMay 1, 2014HYBRID OA

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

ABAndrew BellKJKelvyn Jones
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Abstract

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…

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Topics & keywords

Topics
Keywords
  • Endogeneity
  • Econometrics
  • Covariate
  • Multilevel model
  • Monte Carlo method
  • Random effects model
  • Panel data
  • Context (archaeology)
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