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

William Horrace and Ronald Oaxaca

Econometrics from University Library of Munich, Germany

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, perhaps, justified for practical reasons. A sequential least squares (SLS) esti-mation 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. An empirical example is provided.

Keywords: Linear Probability Model; Sequential Least Squares; Consistency; Monte Carlo (search for similar items in EconPapers)
JEL-codes: C13 C25 (search for similar items in EconPapers)
Pages: 43 pages
Date: 2002-06-19, Revised 2003-05-11
New Economics Papers: this item is included in nep-ecm
Note: Type of Document - Acrobat PDF; prepared on IBM PC; to print on HP; pages: 43; figures: included. A new estimation technique for the LPM model
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Citations: View citations in EconPapers (7)

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Working Paper: New Wine in Old Bottles: A Sequential Estimation Technique for the LPM (2003)
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpem:0206002

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