Fixed and Random Effects in Classical and Bayesian Regression

This paper proposes a common and tractable framework for analyzing different definitions of fixed and random effects in a constant-slope variable-intercept model. It is shown that, regardless of whether effects (i) are treated as parameters or as an error term, (ii) are estimated in different stages of a hierarchical model, or whether (iii) correlation between effects and regressor is allowed, when the same information on effects is introduced into all estimation methods, the resulting slope estimator is also the same across methods. If different methods produce different results, is is ultimately because different information is being used for each method.(This abstract was borrowed from another version of this item.)