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the claim
Random effects estimators require orthogonality between individual effects and regressors.
the verdict
SUPPORTED
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the weight of evidence
2 sources for · 0 against

Random effects estimators in panel data and multilevel modeling conventionally assume that individual-specific effects are orthogonal to the regressors; relaxing this assumption requires specific correlated random effects approaches.

Evidence for · 2
On Ignoring the Random Effects Assumption in Multilevel Models: Review, Critique, and Recommendations
2019 · cited by 185
This paper highlights that standard multilevel models assume random effects are uncorrelated with regressors, and violating this assumption causes endogeneity.
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The analysis

The retrieved papers support the claim that standard random effects models assume independence (orthogonality) between individual effects and regressors, and that violations of this assumption lead to endogeneity or require specialized correlated random effects models.

More for · 1
Spatial dynamic panel data models with correlated random effects
2020 · cited by 25
This paper discusses spatial dynamic panel data models utilizing correlated random effects when unobserved effects are assumed to be correlated with observed time-varying regressors.
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first checked04 Aug 2026
judged → SUPPORTED · 8904 Aug 2026
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