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Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles

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  • Brownston, David
  • Bunch, David S.
  • Train, Kenneth

Abstract

We compare multinomial logit and mixed logit models for data on California households' revealed and stated preferences for automobiles. The stated preference (SP) data elicited households' preferences among gasoline, electric, methanol, and compressed natural gas vehicles with various attributes. The mixed logit models provide improved fits over logit that are highly significant, and show large heterogeneity in respondents' preferences for alternative-fuel vehicles. The effects of including this heterogeneity are demonstrated in forecasting exercises. The alternative-fuel vehicle models presented here also highlight the advantages of merging SP and revealed preference (RP) data. RP data appear to be critical for obtaining realistic body-type choice and scaling information, but they are plagued by multicollinearity and difficulties with measuring vehicle attributes. SP data are critical for obtaining information about attributes not available in the marketplace, but pure SP models with these data give implausible forecasts.

Suggested Citation

  • Brownston, David & Bunch, David S. & Train, Kenneth, 1999. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," University of California Transportation Center, Working Papers qt7rf7s3nx, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt7rf7s3nx
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    References listed on IDEAS

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    1. Kenneth Train, "undated". "Simulation Methods for Probit and Related Models Based on Convenient Error Partitioning," Working Papers _009, University of California at Berkeley, Econometrics Laboratory Software Archive.
    2. 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.
    3. Brownstone, David & Bunch, David S. & Golob, Thomas F. & Ren, Weiping, 1996. "A Transaction Choice Model for Forecasting Demand for Alternative-Fuel Vehicles," University of California Transportation Center, Working Papers qt0244r8g2, University of California Transportation Center.
    4. Lee, Lung-Fei, 1992. "On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models," Econometric Theory, Cambridge University Press, vol. 8(4), pages 518-552, December.
    5. Bunch, David S., 1988. "A comparison of algorithms for maximum likelihood estimation of choice models," Journal of Econometrics, Elsevier, vol. 38(1-2), pages 145-167.
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    2. Lee, Yinjin & Bateman, Alexis, 2021. "The competitiveness of fair trade and organic versus conventional coffee based on consumer panel data," Ecological Economics, Elsevier, vol. 184(C).
    3. Proost, Stef & Van Dender, Kurt, 2012. "Energy and environment challenges in the transport sector," Economics of Transportation, Elsevier, vol. 1(1), pages 77-87.
    4. Yang, Xiaofang & Jin, Wen & Jiang, Hai & Xie, Qianyan & Shen, Wei & Han, Weijian, 2017. "Car ownership policies in China: Preferences of residents and influence on the choice of electric cars," Transport Policy, Elsevier, vol. 58(C), pages 62-71.
    5. Sheng, Hongyan, 1999. "A Dynamic household Alternative-fuel Vehicle Demand Model Using Stated and Revealed Transaction Information," University of California Transportation Center, Working Papers qt0zp4g99j, University of California Transportation Center.

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