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Forecasting adoption of ultra-low-emission vehicles using Bayes estimates of a multinomial probit model and the GHK simulator

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dc.contributor.authorDaziano, Ricardo
dc.contributor.authorAchtnicht, Martin
dc.date.accessioned2013-01-08T21:28:58Z
dc.date.available2013-01-08T21:28:58Z
dc.date.issued2013
dc.description.abstractIn this paper we use Bayes estimates of a multinomial probit model with fully flexible substitution patterns to forecast consumer response to ultra-low-emission vehicles. In this empirical application of the probit Gibbs sampler, we use stated-preference data on vehicle choice from a Germany-wide survey of potential light-duty-vehicle buyers using computer-assisted personal interviewing. We show that Bayesian estimation of a multinomial probit model with a full covariance matrix is feasible for this medium-scale problem and provides results that are very similar to maximum simulated likelihood estimates. Using the posterior distribution of the parameters of the vehicle choice model as well as the GHK simulator we derive the choice probabilities of the different alternatives. We first show that the Bayes point estimates of the market shares reproduce the observed values. Then, we define a base scenario of vehicle attributes that aims at representing an average of the current vehicle choice situation in Germany. Consumer response to qualitative changes in the base scenario is subsequently studied. In particular, we analyze the effect of increasing the network of service stations for charging electric vehicles as well as for refueling hydrogen. The result is the posterior distribution of the choice probabilities that represent adoption of the energy-efficient technologies.en_US
dc.description.sponsorshipNational Science Foundation Faculty Early Career Development CAREER Award No. CBET-1253475en_US
dc.identifier.citationDaziano, RA and Achtnicht, M. (2013). Forecasting adoption of ultra-low-emission vehicles using Bayes estimates of a multinomial probit model and the GHK simulator. Accepted for publication in Transportation Science.en_US
dc.identifier.urihttps://hdl.handle.net/1813/30863
dc.language.isoen_USen_US
dc.relation.hasversionPreliminary version: Daziano, Ricardo A., Martin Achtnicht. 2012. "Forecasting Adoption of Ultra-low-emission Vehicles Using the GHK Simulator and Bayes Estimates of a Multinomial Probit Model". Mannheim: Discussion Paper 12-017 Centre for European Economic Research ZEW.en_US
dc.subjectdiscrete choiceen_US
dc.subjectelectric vehiclesen_US
dc.subjectBayesian econometricsen_US
dc.subjectultra low emission vehiclesen_US
dc.subjectcharging infrastructureen_US
dc.titleForecasting adoption of ultra-low-emission vehicles using Bayes estimates of a multinomial probit model and the GHK simulatoren_US
dc.typetechnical reporten_US

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