Pub. date: 2011 | Online Pub. Date: October 04, 2011 | DOI: 10.4135/9781412994163 | Print ISBN: 9781412959636 | Online ISBN: 9781412994163| Publisher:SAGE Publications, Inc.About this encyclopedia
Misspecification is a fundamental problem in empirical modeling. The origins of this problem can be found in the theoretical exercise of using a statistical model from a sample to make inferences about an unobservable population of interest. Any deviation from the true population model in the sample model means that the sample model is misspecified. This, in turn, means that the inferences from the sample model about the population are suspect. The importance of problems of misspecification is underscored by the amount of attention paid to different types of misspecification in introductory texts on ordinary least squares (OLS) regression models. As an example, consider Damodar Gujarati's widely used textbook, Basic Econometrics . Gujarati's treatment of OLS is centered around 10 assumptions of the linear regression model. Six of these 10 assumptions are statements that the model does not contain one or more types of misspecification. Discussed throughout this entry is ...