Misspecified Mean Function Regression: Making Good Use of Regression Models That Are Wrong

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 altern...

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VerfasserInnen: Berk, Richard A (Verfasst von) ; Zhao, Linda (Verfasst von) ; Zhang, Kai (Verfasst von) ; Pitkin, Emil (Verfasst von) ; George, Edward I (Verfasst von) ; Buja, Andreas (Verfasst von) ; Brown, Lawrence D (Verfasst von)
Medienart: Elektronisch Buch
Sprache:Englisch
Veröffentlicht: 2014
In:Jahr: 2014
Online-Zugang: Volltext (kostenfrei)
Verfügbarkeit prüfen: HBZ Gateway
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Zusammenfassung: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