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

William Horrace and Ronald Oaxaca

No 703, IZA Discussion Papers from Institute of Labor Economics (IZA)

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 (search for similar items in EconPapers)
JEL-codes: C25 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2003-01
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-pke
References: View references in EconPapers View complete reference list from CitEc
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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