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Forecasting automobile demand using disaggregate choice models

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  • Berkovec, James
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    Abstract

    This paper presents a simulation model of the American automobile market. The simulation model combines a disaggregate model of household automobile number and type choice with an econometric model of used vehicle scrappage and simple models of new car supply. For fixed vehicle designs, consumer and producer interactions determine new car sales, used car scrappage and consumer vehicle holdings. The model allows automobiles to be highly differentiated and consumers to be heterogeneous. Short-run equilibrium is defined as supply equal to demand for every vehicle type during each market period. The automobile stock then evolves slowly as new vehicles are added and old vehicles are removed during each period. An empirical application of the simulation model with 12 consumer groups and 131 vehicle types is used to forecast automobile holdings. A base case scenario is run for 1978-1984 and compared with the observed market behavior during this period. Several other simulations are then run comparing different gasoline price scenarios with the base case for 1984-1990.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part B: Methodological.

    Volume (Year): 19 (1985)
    Issue (Month): 4 (August)
    Pages: 315-329

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    Handle: RePEc:eee:transb:v:19:y:1985:i:4:p:315-329

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    Cited by:
    1. Choo, Sangho & Mokhtarian, Patricia L., 2004. "What type of vehicle do people drive? The role of attitude and lifestyle in influencing vehicle type choice," University of California Transportation Center, Working Papers qt7vg1057g, University of California Transportation Center.
    2. Bhat, Chandra R. & Sen, Sudeshna, 2006. "Household vehicle type holdings and usage: an application of the multiple discrete-continuous extreme value (MDCEV) model," Transportation Research Part B: Methodological, Elsevier, vol. 40(1), pages 35-53, January.
    3. Musti, Sashank & Kockelman, Kara M., 2011. "Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(8), pages 707-720, October.
    4. Martin, Elliot William, 2009. "New Vehicle Choices, Fuel Economy and Vehicle Incentives: An Analysis of Hybrid Tax Credits and Gasoline Tax," University of California Transportation Center, Working Papers qt6sz198c2, University of California Transportation Center.
    5. Baltas, George & Saridakis, Charalampos, 2013. "An empirical investigation of the impact of behavioural and psychographic consumer characteristics on car preferences: An integrated model of car type choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 54(C), pages 92-110.
    6. Martin, Elliott William, 2009. "New Vehicle Choice, Fuel Economy and Vehicle Incentives: An Analysis of Hybrid Tax Credits and the Gasoline Tax," University of California Transportation Center, Working Papers qt5gd206wv, University of California Transportation Center.
    7. Sa-ngasoongsong, Akkarapol & Bukkapatnam, Satish T.S. & Kim, Jaebeom & Iyer, Parameshwaran S. & Suresh, R.P., 2012. "Multi-step sales forecasting in automotive industry based on structural relationship identification," International Journal of Production Economics, Elsevier, vol. 140(2), pages 875-887.
    8. Bhat, Chandra R. & Sen, Sudeshna & Eluru, Naveen, 2009. "The impact of demographics, built environment attributes, vehicle characteristics, and gasoline prices on household vehicle holdings and use," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 1-18, January.
    9. Hirota, Keiko, 2007. "Policy for better Air Quality in Asia: Proposal for a Policy Evaluation Method for four ASEAN Countries," MPRA Paper 15081, University Library of Munich, Germany, revised 04 Oct 2007.
    10. Golob, Thomas F. & Kim, Seyoung & Ren, Weiping, 1996. "How households use different types of vehicles: A structural driver allocation and usage model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(2), pages 103-118, March.
    11. Chen, Anning, 2011. "Reliable GPS Integer Ambiguity Resolution," University of California Transportation Center, Working Papers qt9gs0t2f9, University of California Transportation Center.

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