TY - JOUR
AU - Jones, Kelvyn
AB - 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.
TI - Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data*
JF - Political Science Research and Methods
DO - 10.1017/psrm.2014.7
DA - 2014-05-01
UR - https://www.deepdyve.com/lp/cambridge-university-press/explaining-fixed-effects-random-effects-modeling-of-time-series-cross-WSDb2tTzKV
SP - 133
EP - 153
VL - 3
IS - 1
DP - DeepDyve
ER -