Advanced choice models

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dc.contributor.author Swait Jr, Joffre en_US
dc.contributor.editor Kanninen, B en_US
dc.date.accessioned 2012-02-02T02:49:19Z
dc.date.available 2012-02-02T02:49:19Z
dc.date.issued 2006 en_US
dc.identifier 2010006517 en_US
dc.identifier.citation Swait Joffre 2006, 'Advanced choice models', in NA (ed.), Springer, Netherlands, pp. 229-293. en_US
dc.identifier.issn 13 978-1-4020-4064-1 en_US
dc.identifier.other B1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/14337
dc.description.abstract In Alberini et al. (this volume), an overview of the workhorse model (the Multinomial Logit, or MNL) for discrete choice analysis was presented from its conception to its practical use in predicting behavior and evaluating welfare impacts. Its highly nonlinear nature allows the MNL to capture a wide variety of compensatory attribute-based tradeoffs and has made this model specification a useful tool for economists, engineers and marketers. Allied to its flexibility is the fact that many general statistical analysis programs, as well as more specialized tools, straightforwardly implement estimation of the MNL model. And it doesn?t hurt that the MNL?s likelihood function is globally concave, implying that the output of these programs are guaranteed to be the maximum likelihood estimates (this is no small advantage in the realm we are about to enter). en_US
dc.language en_US
dc.publisher Springer en_US
dc.relation.isbasedon NA en_US
dc.title Advanced choice models en_US
dc.parent Valuing Environmental Amenities Using Stated Choice Studies: A Common Sense Approach to Theory and Practice en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Netherlands en_US
dc.identifier.startpage 229 en_US
dc.identifier.endpage 293 en_US
dc.cauo.name BUS.Centre for the Study of Choice en_US
dc.conference Verified OK en_US
dc.for 140302 en_US
dc.personcode 100729 en_US
dc.percentage 100 en_US
dc.classification.name Econometric and Statistical Methods en_US
dc.classification.type FOR-08 en_US
dc.edition 1st en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords NA en_US
dc.staffid 100729 en_US


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