On the effect of the prior of Bayes estimators of the willingness to pay for electric-vehicle driving range |
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| Institution: | 1. KTH – Royal Institute of Technology, Department of Chemical Engineering, Division of Energy Processes, Teknikringen 50, SE-100 44 Stockholm, Sweden;2. Swedish National Road and Transport Research Institute, SE-583 30 Linköping, Sweden;3. Linköping University, Department of Management and Engineering, Division of Political Science, SE-581 83 Linköping, Sweden;1. KTH – Royal Institute of Technology, Department of Chemical Engineering and Technology, Division of Energy Processes, Stockholm, Sweden;2. Molde University College – Specialized University in Logistics, Faculty of Business Administration and Social Sciences, Molde, Norway;1. Institute of Chinese Studies, Freie Universität Berlin, Fabeckstr. 23-25, 14195 Berlin, Germany;2. Skalitzer Str. 100, 10997 Berlin, Germany;1. Department of Management and Humanities, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia;2. Department of Management & Humanities, Universiti Teknologi PETRONAS Bandar Seri Iskandar, Tronoh 31750, Perak, Malaysia;3. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA,;4. Department of Management and Sciences, University of Loralai, Pakistan;1. CN Professor of SCM, University of Manitoba, 484 Drake Centre, Winnipeg, Manitoba R3T 5V4, Canada;2. Via < = > Fara Transportation Policy and Planning, Winnipeg, Manitoba, Canada;3. Centre for Emerging Renewable Energy Inc. (CERE), Canada;4. Elias Consulting, Victoria, British Columbia, Canada;1. Environmental Policy Research Group, Korea Environment Institute, 370 Sicheong-daero, Sejong-si 339-007, South Korea;2. The University of Texas at Austin, Department of Civil, Architectural and Environmental Engineering, 301 E. Dean Keeton St. Stop C1761, Austin, TX 78712, United States;3. King Abdulaziz University, Jeddah 21589, Saudi Arabia;4. Georgia Institute of Technology, School of Civil and Environmental Engineering, Mason Building, 790 Atlantic Drive, Atlanta, GA 30332-0355, United States |
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| Abstract: | We use Bayes’ estimator of a consumer-surplus probit model to study the relevance of the prior in a discrete choice model. We take random subsamples of varying sizes of stated preference data regarding ultra-low emission vehicle purchases in California and focus on the willingness-to-pay for improvements in driving range. Prior information is obtained from a meta-analysis of consumer valuation of driving range. We find the posterior distribution of the willingness-to-pay using a tight and a weakly informative prior, and also analyze the nonparametric estimates of the posterior compare these with the likelihood function of the problem. It is found that the weight of the prior is relevant for very small samples, but for standard sample sizes the prior vanishes. Thus, the Bayes estimator of a static discrete choice model is in general equivalent to the maximum likelihood estimator, although for some intermediate sample sizes the prior provides more realistic values. |
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