Misspecified Mean Function Regression

Richard Berk, Lawrence Brown, Andreas Buja, Edward George, Emil Pitkin, Kai Zhang and Linda Zhao

Sociological Methods & Research, 2014, vol. 43, issue 3, 422-451

Abstract: There are over three decades of largely unrebutted criticism of regression analysis as practiced in the social sciences. Yet, regression analysis broadly construed remains for many the method of choice for characterizing conditional relationships. One possible explanation is that the existing alternatives sometimes can be seen by researchers as unsatisfying. In this article, we provide a different formulation. We allow the regression model to be incorrect and consider what can be learned nevertheless. To this end, the search for a correct model is abandoned. We offer instead a rigorous way to learn from regression approximations. These approximations, not “the truth,†are the estimation targets. There exist estimators that are asymptotically unbiased and standard errors that are asymptotically correct even when there are important specification errors. Both can be obtained easily from popular statistical packages.

Keywords: random predictors; linear models; model misspecification; regression models; misspecified mean function regression (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:43:y:2014:i:3:p:422-451

DOI: 10.1177/0049124114526375

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