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Selection Bias in Linear Regression, Logit and Probit Models

Dubin, Jeffrey A. and Rivers, Douglas (1989) Selection Bias in Linear Regression, Logit and Probit Models. Social Science Working Paper, 702. California Institute of Technology , Pasadena, CA. (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20170905-134432262

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Abstract

Missing data are common in observational studies due to self-selection of subjects. Missing data can bias estimates of linear regression and related models. The nature of selection bias and econometric methods for correcting it are described. The econometric approach relies upon a specification of the selection mechanism. We extend this approach to binary logit and probit models and provide a simple test for selection bias in these models. An analysis of candidate preference in the 1984 U.S. presidential election illustrates the technique.


Item Type:Report or Paper (Working Paper)
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http://resolver.caltech.edu/CaltechAUTHORS:20170906-143926487Related ItemPublished Version
Additional Information:Published as Dubin, Jeffrey A., and Douglas Rivers. "Selection bias in linear regression, logit and probit models." Sociological Methods & Research 18, no. 2-3 (1989): 360-390.
Group:Social Science Working Papers
Record Number:CaltechAUTHORS:20170905-134432262
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20170905-134432262
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:81145
Collection:CaltechAUTHORS
Deposited By: Jacquelyn Bussone
Deposited On:05 Sep 2017 22:41
Last Modified:06 Sep 2017 21:45

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