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dc.contributor.authorHorrace, William C.en_US
dc.contributor.authorOaxaca, Ronald L.en_US
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.publisherInstitute for the Study of Labor (IZA) Bonnen_US
dc.relation.ispartofseriesIZA Discussion paper series 703en_US
dc.subject.keywordlinear probability modelen_US
dc.subject.keywordsequential least squaresen_US
dc.subject.keywordMonte Carloen_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
Appears in Collections:IZA Discussion Papers, Forschungsinstitut zur Zukunft der Arbeit (IZA)

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