Predictive Efficiency Of Random Effects Approach: A Real Model Simulation Study

Abstract

This real model simulation study attempts to shed more light on the predictive performances of two of the most commonly used panel data regression methods - fixed effects and random effects.  In particular, this paper attempts to address the question, “How do these two alternative estimators perform in prediction when errors follow non-normal distributions?”  The simulation results support the random effects approach as the better choice.