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

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  • Daziano, Ricardo A.
  • Achtnicht, Martin

Abstract

In 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. 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-effcient technologies.

Suggested Citation

  • Daziano, Ricardo A. & Achtnicht, Martin, 2012. "Forecasting adoption of ultra-low-emission vehicles using the GHK simulator and Bayes estimates of a multinomial probit model," ZEW Discussion Papers 12-017, ZEW - Leibniz Centre for European Economic Research.
    • Handle: RePEc:zbw:zewdip:12017
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    References listed on IDEAS

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    1. Bolduc, D. & Ben-Akiva, M., 1991. "A Multinational Probit Formulation for Large Choice Sets," Papers 9110, Laval - Recherche en Energie.
    2. Denis Bolduc & Bernard Fortin & Stephen Gordon, 1997. "Multinomial Probit Estimation of Spatially Interdependent Choices: An Empirical Comparison of Two New Techniques," International Regional Science Review, , vol. 20(1-2), pages 77-101, April.
    3. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
    4. Hensher, David A., 2010. "Hypothetical bias, choice experiments and willingness to pay," Transportation Research Part B: Methodological, Elsevier, vol. 44(6), pages 735-752, July.
    5. Dansie, B. R., 1985. "Parameter estimability in the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 19(6), pages 526-528, December.
    6. Geweke, John & Keane, Michael P & Runkle, David, 1994. "Alternative Computational Approaches to Inference in the Multinomial Probit Model," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 609-632, November.
    7. Bunch, David S. & Bradley, Mark & Golob, Thomas F. & Kitamura, Ryuichi & Occhiuzzo, Gareth P., 1993. "Demand for clean-fuel vehicles in California: A discrete-choice stated preference pilot project," Transportation Research Part A: Policy and Practice, Elsevier, vol. 27(3), pages 237-253, May.
    8. McCulloch, Robert E. & Polson, Nicholas G. & Rossi, Peter E., 2000. "A Bayesian analysis of the multinomial probit model with fully identified parameters," Journal of Econometrics, Elsevier, vol. 99(1), pages 173-193, November.
    9. Brownstone, David & Train, Kenneth, 1998. "Forecasting new product penetration with flexible substitution patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 109-129, November.
    10. Brownstone, David & Bunch, David S. & Train, Kenneth, 2000. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 34(5), pages 315-338, June.
    11. Horne, Matt & Jaccard, Mark & Tiedemann, Ken, 2005. "Improving behavioral realism in hybrid energy-economy models using discrete choice studies of personal transportation decisions," Energy Economics, Elsevier, vol. 27(1), pages 59-77, January.
    12. Martin Achtnicht, 2012. "German car buyers’ willingness to pay to reduce CO 2 emissions," Climatic Change, Springer, vol. 113(3), pages 679-697, August.
    13. Vassilis A. Hajivassiliou & Daniel L. McFadden, 1998. "The Method of Simulated Scores for the Estimation of LDV Models," Econometrica, Econometric Society, vol. 66(4), pages 863-896, July.
    14. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    15. Brownstone, David & Bunch, David S. & Train, Kenneth, 2000. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 34(5), pages 315-338, June.
    16. Bunch, David S., 1991. "Estimability in the Multinomial Probit Model," University of California Transportation Center, Working Papers qt1gf1t128, University of California Transportation Center.
    17. Brownstone, David & Bunch, David S & Golob, Thomas F & Ren, Weiping, 1996. "A Transactions Choice Model for Forecasting Demand for Alternative-Fuel Vehicles," University of California Transportation Center, Working Papers qt3sm7w9zk, University of California Transportation Center.
    18. Geweke, John F. & Keane, Michael P. & Runkle, David E., 1997. "Statistical inference in the multinomial multiperiod probit model," Journal of Econometrics, Elsevier, vol. 80(1), pages 125-165, September.
    19. Nobile, Agostino, 2000. "Comment: Bayesian multinomial probit models with a normalization constraint," Journal of Econometrics, Elsevier, vol. 99(2), pages 335-345, December.
    20. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    21. David Hensher & Matthew Beck & John Rose, 2011. "Accounting for Preference and Scale Heterogeneity in Establishing Whether it Matters Who is Interviewed to Reveal Household Automobile Purchase Preferences," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 49(1), pages 1-22, May.
    22. Moore, William L & Holbrook, Morris B, 1990. "Conjoint Analysis on Objects with Environmentally Correlated Attributes: The Questionable Importance of Representative Design," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(4), pages 490-497, March.
    23. Bunch, David S., 1991. "Estimability in the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 25(1), pages 1-12, February.
    24. Mariano,Roberto & Schuermann,Til & Weeks,Melvyn J. (ed.), 2000. "Simulation-based Inference in Econometrics," Cambridge Books, Cambridge University Press, number 9780521591126.
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    3. Hackbarth, André & Madlener, Reinhard, 2011. "Consumer Preferences for Alternative Fuel Vehicles: A Discrete Choice Analysis," FCN Working Papers 20/2011, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    4. Iogansen, Xiatian & Wang, Kailai & Bunch, David & Matson, Grant & Circella, Giovanni, 2023. "Deciphering the factors associated with adoption of alternative fuel vehicles in California: An investigation of latent attitudes, socio-demographics, and neighborhood effects," Transportation Research Part A: Policy and Practice, Elsevier, vol. 168(C).
    5. Hackbarth, André & Madlener, Reinhard, 2016. "Willingness-to-pay for alternative fuel vehicle characteristics: A stated choice study for Germany," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 89-111.

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    More about this item

    Keywords

    Discrete choice models; Bayesian econometrics; Low emission vehicles; Charging infrastructure;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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