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dc.contributor.authorHorowitz, Joel L.en_US
dc.date.accessioned2009-01-29T14:54:15Z-
dc.date.available2009-01-29T14:54:15Z-
dc.date.issued2004en_US
dc.identifier.urihttp://hdl.handle.net/10419/22191-
dc.description.abstractMuch empirical research is concerned with estimating conditional mean, median, or hazard functions. For example, labor economists are interested in estimating the mean wages of employed individuals conditional on characteristics such as years of work experience and education. The most frequently used estimation methods assume that the function of interest is known up to a set of constant parameters that can be estimated from data. Models in which the only unknown quantities are a finite set of constant parameters are called parametric. The use of a parametric model greatly simplifies estimation, statistical inference, and interpretation of the estimation results but is rarely justified by theoretical or other a priori considerations. Estimation and inference based on convenient but incorrect assumptions about the form of the conditional mean function can be highly misleading.en_US
dc.language.isoengen_US
dc.publisheren_US
dc.relation.ispartofseriesPapers / Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE) 2004,17en_US
dc.subject.ddc330en_US
dc.titleSemiparametric modelsen_US
dc.typeWorking Paperen_US
dc.identifier.ppn495306592en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.identifier.repecRePEc:zbw:caseps:200417-
Appears in Collections:Papers, CASE - Center for Applied Statistics and Economics, HU Berlin

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