Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data*
Abstrak
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.
Topik & Kata Kunci
Penulis (2)
Andrew Bell
K. Jones
Akses Cepat
- Tahun Terbit
- 2014
- Bahasa
- en
- Total Sitasi
- 1429×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1017/psrm.2014.7
- Akses
- Open Access ✓