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Multiple regression with a qualitative dependent variable

dc.contributor.authorRubinfeld, Daniel L.en_US
dc.date.accessioned2006-04-07T17:55:45Z
dc.date.available2006-04-07T17:55:45Z
dc.date.issued1982en_US
dc.identifier.citationRubinfeld, Daniel L. (1982)."Multiple regression with a qualitative dependent variable." Journal of Economics and Business 34(1): 67-78. <http://hdl.handle.net/2027.42/24088>en_US
dc.identifier.urihttp://www.sciencedirect.com/science/article/B6V7T-45F9N6W-M/2/6be474463973ff5343828d354a57b2bcen_US
dc.identifier.urihttps://hdl.handle.net/2027.42/24088
dc.description.abstractThis paper describes several methods by which a single set of scores for a qualitative (usually ordinal) dependent variable can be estimated simultaneously with the coefficients of the explanatory variables of a model. The canonical correlations and multiple discriminant analysis approaches are well known in the statistics literature. However, the paper goes on to show an iterative least-squares multiple regression technique can provide a useful approximation to these more general procedures. The techniques are illustrated with labor force participation and voter turnout examples.en_US
dc.format.extent1737680 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherElsevieren_US
dc.titleMultiple regression with a qualitative dependent variableen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelSocial Sciences (General)en_US
dc.subject.hlbsecondlevelAmerican and Canadian Studiesen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.subject.hlbtoplevelHumanitiesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumUniversity of Michigan, Ann Arbor, Michigan, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/24088/1/0000344.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1016/0148-6195(82)90018-2en_US
dc.identifier.sourceJournal of Economics and Businessen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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