The Consequences Of Model Misspecification In Regression Analysis

Abstract

In ordinary least squares regression analysis the desired property of unbiasedness in estimated coefficients is contingent upon the correspondence of the fitted model with the true underlying data generating process. This paper focuses on developing a systematic characterization of the error forms resulting from model misspecification in single equation models. The consequences of model misspecification, for the error forms identified, are also evaluated.