Can someone please explain why bother using random effects if the unobserved constant effects are assumed to not be correlated with the explanatory variable? Why not just using a pooled OLS?

  • $\begingroup$ The Anova modeling appears to invoke random-effects assumption by assuming that unobserved random effects are not correlated with explanatory variables. This type of modeling I.e. Anova finally tells whether the overal model explains the dependent variable "sufficiently" or inadequately. The OLS model explains the role of individual separate variables as well as overall R-square. The two approaches are distinct and make unique assumptions.The advent of mixed effects and many variants of regression seem to add to the confusion of younger researchers. $\endgroup$ – Subhash C. Davar Nov 27 '18 at 12:43

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