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Forecasting adoption of ultra-low-emission vehicles using the GHK simulator and bayes estimates of a multinomial probit model
Daziano, Ricardo A.
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Achtnicht, Martin
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URL:
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https://ub-madoc.bib.uni-mannheim.de/31396
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URN:
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urn:nbn:de:bsz:180-madoc-313968
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Dokumenttyp:
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Arbeitspapier
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Erscheinungsjahr:
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2012
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Titel einer Zeitschrift oder einer Reihe:
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ZEW Discussion Papers
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Band/Volume:
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12-017
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Ort der Veröffentlichung:
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Mannheim
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Sonstige Einrichtungen > ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung
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MADOC-Schriftenreihe:
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Veröffentlichungen des ZEW (Leibniz-Zentrum für Europäische Wirtschaftsforschung) > ZEW Discussion Papers
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Fachgebiet:
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330 Wirtschaft
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Fachklassifikation:
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JEL:
C25 , D12 , Q42,
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Normierte Schlagwörter (SWD):
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Deutschland , Elektrofahrzeug , Wasserstoff , Konsumentenverhalten , Innovationsdiffusion , Diskrete Entscheidung , Probit-Modell , Bayes-Statistik , Schätzung
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Freie Schlagwörter (Englisch):
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discrete choice models , bayesian econometrics , low emission vehicles , charging infrastructure
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Abstract:
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In this paper we use Bayes estimates of a multinomial probit model with fully exible substitution patterns to forecast consumer response to ultra-low-emission vehicles. In this empirical application of the probit Gibbs sampler, we use statedpreference data on vehicle choice from a Germany-wide survey of potential lightduty-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. 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.
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