New Wine in Old Bottles: A Sequential Estimation Technique for the Lpm

34 Pages Posted: 11 Apr 2003

See all articles by William C. Horrace

William C. Horrace

Syracuse University - Department of Economics

Ronald L. Oaxaca

University of Arizona - Department of Economics; IZA Institute of Labor Economics

Date Written: January 2003

Abstract

The conditions under which ordinary least squares (OLS) is an unbiased and consistent estimator of the linear probability model (LPM) are unlikely to hold in many instances. Yet the LPM still may be the correct model or a good approximation to the probability generating process. A sequential least squares (SLS) estimation procedure is introduced that may outperform OLS in terms of finite sample bias and yields a consistent estimator. Monte Carlo simulations reveal that SLS outperforms OLS, probit and logit in terms of mean squared error of the predicted probabilities.

Keywords: Linear Probability Model, Sequential Least Squares, Consistency, Monte Carlo

JEL Classification: C25

Suggested Citation

Horrace, William C. and Oaxaca, Ronald L., New Wine in Old Bottles: A Sequential Estimation Technique for the Lpm (January 2003). IZA Discussion Paper No. 703. Available at SSRN: https://ssrn.com/abstract=383102

William C. Horrace

Syracuse University - Department of Economics ( email )

Syracuse, NY 13244-1020
United States
315-443-9061 (Phone)
315-443-1081 (Fax)

HOME PAGE: http://faculty.maxwell.syr.edu/whorrace

Ronald L. Oaxaca (Contact Author)

University of Arizona - Department of Economics ( email )

McClelland Hall
Tucson, AZ 85721-0108
United States
520-621-4135 (Phone)
520-621-8450 (Fax)

IZA Institute of Labor Economics

P.O. Box 7240
Bonn, D-53072
Germany

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