Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/20634 
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dc.contributor.authorHorrace, William C.en
dc.contributor.authorOaxaca, Ronald L.en
dc.date.accessioned2009-01-28T16:15:34Z-
dc.date.available2009-01-28T16:15:34Z-
dc.date.issued2003-
dc.identifier.urihttp://hdl.handle.net/10419/20634-
dc.description.abstractThe 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.en
dc.language.isoengen
dc.publisher|aInstitute for the Study of Labor (IZA) |cBonnen
dc.relation.ispartofseries|aIZA Discussion Papers |x703en
dc.subject.jelC25en
dc.subject.ddc330en
dc.subject.keywordlinear probability modelen
dc.subject.keywordsequential least squaresen
dc.subject.keywordconsistencyen
dc.subject.keywordMonte Carloen
dc.subject.stwRegressionen
dc.subject.stwSchätztheorieen
dc.subject.stwTheorieen
dc.subject.stwLinear Probability Modelen
dc.titleNew Wine in Old Bottles: A Sequential Estimation Technique for the LPM-
dc.typeWorking Paperen
dc.identifier.ppn362201730en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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