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Cited by 1,515

The DOI matches its Crossref record; 97 of the 97 references it cites were found in a registry or an index.

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

Open access
Bell, Andrew, Jones, Kelvyn·2015·Political Science Research and Methods
DOI 10.1017/psrm.2014.7
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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 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.

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Metadata

Authors
Bell, A., & Jones, K.
Venue
Political Science Research and Methods
Publisher
Cambridge University Press (CUP)
Published
2014-05-01
Volume
3
Issue
1
Pages
133-153
ISSN
  • 2049-8470 (ISSN-L)
  • 2049-8489
DOI
10.1017/psrm.2014.7
OpenAlex
W2118248224
Cited by
1,515
References
97
FWCI
172.94
Type
journal article
Language
en
Open access
hybrid

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