Statistics Papers
Document Type
Journal Article
Date of this Version
8-2014
Publication Source
Sociological Methods Research
Volume
43
Issue
3
Start Page
422
Last Page
451
DOI
10.1177/0049124114526375
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
Recommended Citation
Berk, R. A., Brown, L. D., Buja, A., George, E. I., Pitkin, E., Zhang, K., & Zhao, L. (2014). Misspecified Mean Function Regression: Making Good Use of Regression Models That Are Wrong. Sociological Methods Research, 43 (3), 422-451. http://dx.doi.org/10.1177/0049124114526375
Date Posted: 27 November 2017
This document has been peer reviewed.