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the claim

Random effects estimators require orthogonality between individual effects and regressors.

the verdict
SUPPORTED
the evidence backs this
Recorded sources
2 sources for · 0 against

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Is it true that random effects estimators require orthogonality between individual effects and regressors?

Yes. 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.

Verdict: SUPPORTED · 2 sources for, 0 against · Last checked

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.

Evidence for · 2
On Ignoring the Random Effects Assumption in Multilevel Models: Review, Critique, and Recommendations
2019
This paper highlights that standard multilevel models assume random effects are uncorrelated with regressors, and violating this assumption causes endogeneity.
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Antonakis, J., Bastardoz, N., & Rönkkö, M. (2019). On Ignoring the Random Effects Assumption in Multilevel Models: Review, Critique, and Recommendations. doi.org. https://doi.org/10.1177/1094428119877457

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More for · 1
Spatial dynamic panel data models with correlated random effects
2020
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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Li, L. & Yang, Z. (2020). Spatial dynamic panel data models with correlated random effects. doi.org. https://doi.org/10.1016/J.JECONOM.2020.05.016

What does the evidence say?

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.

Receipt History
first checked04 Aug 2026
judged → SUPPORTED · 8904 Aug 2026
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