Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/31341
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dc.contributor.authorCameron, A. Colinen_US
dc.date.accessioned2006-03-08en_US
dc.date.accessioned2010-05-14T11:04:09Z-
dc.date.available2010-05-14T11:04:09Z-
dc.date.issued2006en_US
dc.identifier.urihttp://hdl.handle.net/10419/31341-
dc.description.abstractA very brief survey of regression for categorical data. Categorical outcome (or discrete outcome or qualitative response) regression models are models for a discrete dependent variable recording in which of two or more categories an outcome of interest lies. For binary data (two categories) probit and logit models or semiparametric methods are used. For multinomial data (more than two categories) that are unordered, common models are multinomial and conditional logit, nested logit, multinomial probit, and random parameters logit. The last two models are estimated using simulation or Bayesian methods. For ordered data, standard multinomial models are ordered logit and probit, or count models are used if ordered discrete data are actually a count.en_US
dc.language.isoengen_US
dc.publisher|aDep. of Economics, Univ. of California |cDavis, Calif.en_US
dc.relation.ispartofseries|aWorking papers // University of California, Department of Economics |x06,12en_US
dc.subject.jelC21en_US
dc.subject.jelC25en_US
dc.subject.ddc330en_US
dc.titleCategorical dataen_US
dc.type|aWorking Paperen_US
dc.identifier.ppn508646359en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-

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