Replication data for: Explaining Fixed Effects: Random Effects modelling of Time-Series Cross-Sectional and Panel Data

This article challenges Fixed Effects (FE) modelling as the 'default™' for time-series-cross-sectional and panel data. Understanding differences between within- and between-effects is crucial when choosing modelling strategies. The downside of Random Effects (RE) modelling (correlated lower-level covariates and higher-level residuals)“ is omitted-variable bias, solvable with Mundlak's (1978a) formulation. Consequently, RE can provide everything FE promises and more, and this is confirmed by Monte-Carlo simulations, which additionally show problems with Pluemper 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 and heterogeneity, modelled 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.