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

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Author Info
Horrace, William C. (Syracuse University)
Oaxaca, Ronald L. () (University of Arizona and IZA Bonn)

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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.

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Publisher Info
Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 703.

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Length: 34 pages
Date of creation: Jan 2003
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Handle: RePEc:iza:izadps:dp703

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Related research
Keywords: linear probability model; sequential least squares; consistency; Monte Carlo;

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Find related papers by JEL classification:
C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models

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  3. Amemiya, Takeshi, 1977. "Some Theorems in the Linear Probability Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(3), pages 645-50, October. [Downloadable!] (restricted)
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  1. Stephen L. Ross & Yves Zenou, 2006. "Are Shirking and Leisure Substitutable? An Empirical Test of Efficiency Wages Based on Urban Economic Theory," Working papers 2006-21, University of Connecticut, Department of Economics. [Downloadable!]
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