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The joint estimation of respondent-reported certainty and acceptability with choice
Institution:1. Institute for Choice, University of South Australia, 140 Arthur St, North Sydney, NSW, Australia;2. Institute of Transport and Logistics Studies, University of Sydney, St James Campus (C13), Sydney, NSW 2006, Australia;1. Lithuanian University of Health Sciences, Academy of Medicine, Department of Neurology, Lithuania;2. Lithuanian University of Health Sciences, Academy of Medicine, Department of Intensive Care, Lithuania;1. Department of Mathematics and System Science, National University of Defense Technology, Changsha, 410073, China;2. Department of Mathematics, Zhejiang University, Hangzhou, 310027, China;1. Virginia Tech, Agricultural and Applied Economics, 306B Hutcheson Hall, Blacksburg, VA 24061, USA;2. Virginia Tech, Agricultural and Applied Economics, 305 Hutcheson Hall, Blacksburg, VA 24061, USA;3. USDA Forest Service, Forestry Sciences Lab, Southern Research Station Research, Triangle Park, NC 27709, USA;4. U.S. Fish and Wildlife Service, Chesapeake Bay Field Office, Fairfax Drive, Room 4020, Arlington, VA 22203, USA
Abstract:In the stated choice literature, increasing attention has been paid to methods that seek to close the gap between the choices from these experiments and the choices experienced in the real world. Attempts to produce model estimates that are truer to real market behaviours are especially important for transportation, where many important policy decisions rely on such experiments. A recent approach that has emerged makes use of a certainty index whereby respondents report how certain they are about each choice they make. Additional literature also posits that when making decisions, people first identify an acceptable set of alternatives (alternative acceptability) such that a consideration set if formed and it is from this reduced set that the ultimate choice is made. This paper presents two models that jointly estimates choice and choice certainty and choice and alternative acceptability. This joint estimation allows the modeller to overcome potential endogeneity that may exist between these responses. In comparing choices of differing certainty, surprisingly little difference in marginal sensitivities are found. This is not the case in the alternative acceptability models however. An important finding of this research is that what could be interpreted as preference heterogeneity may in fact be more closely linked to scale. The ramifications of these results on future research are discussed.
Keywords:Certainty calibration  Vehicle choice  Joint estimation  Heterogeneity  Choice certainty  Alternative acceptability
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