Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/20634
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dc.contributor.authorHorrace, William C.en_US
dc.contributor.authorOaxaca, Ronald L.en_US
dc.date.accessioned2009-01-28T16:15:34Z-
dc.date.available2009-01-28T16:15:34Z-
dc.date.issued2003en_US
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_US
dc.language.isoengen_US
dc.publisher|aInstitute for the Study of Labor (IZA) |cBonnen_US
dc.relation.ispartofseries|aIZA Discussion paper series |x703en_US
dc.subject.jelC25en_US
dc.subject.ddc330en_US
dc.subject.keywordlinear probability modelen_US
dc.subject.keywordsequential least squaresen_US
dc.subject.keywordconsistencyen_US
dc.subject.keywordMonte Carloen_US
dc.subject.stwRegressionen_US
dc.subject.stwSch├Ątztheorieen_US
dc.subject.stwTheorieen_US
dc.subject.stwLinear Probability Modelen_US
dc.titleNew Wine in Old Bottles: A Sequential Estimation Technique for the LPMen_US
dc.typeWorking Paperen_US
dc.identifier.ppn362201730en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-

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