[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/cdl/uctcwp/qt3tb6j874.html
   My bibliography  Save this paper

Forecasting new product penetration with flexible substitution patterns

Author

Listed:
  • Brownstone, David
  • Train, Kenneth
Abstract
We describe and apply choice models, including generalizations of logit called mixed logits, that do not exhibit the restrictive independence from irrelevant alternatives property and can approximate any substitution pattern. The models are estimated on data from a stated-preference survey that elicited customers preferences among gas, electric, methanol, and CNG vehicles with various attributes.

Suggested Citation

  • Brownstone, David & Train, Kenneth, 1999. "Forecasting new product penetration with flexible substitution patterns," University of California Transportation Center, Working Papers qt3tb6j874, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt3tb6j874
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/3tb6j874.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. McFadden, Daniel & Ruud, Paul A, 1994. "Estimation by Simulation," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 591-608, November.
    3. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    4. 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.
    5. Terry Elrod, 1988. "Choice Map: Inferring a Product-Market Map from Panel Data," Marketing Science, INFORMS, vol. 7(1), pages 21-40.
    6. Tülin Erdem, 1996. "A Dynamic Analysis of Market Structure Based on Panel Data," Marketing Science, INFORMS, vol. 15(4), pages 359-378.
    7. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    8. Stern, Steven, 1992. "A Method for Smoothing Simulated Moments of Discrete Probabilities in Multinomial Probit Models," Econometrica, Econometric Society, vol. 60(4), pages 943-952, July.
    9. 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.
    10. Kenneth E. Train, 1998. "Recreation Demand Models with Taste Differences over People," Land Economics, University of Wisconsin Press, vol. 74(2), pages 230-239.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Joan L. Walker & Moshe Ben-Akiva, 2011. "Advances in Discrete Choice: Mixture Models," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 8, Edward Elgar Publishing.
    3. Brownstone, David & Bunch, David S & Train, Kenneth, 1999. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," Department of Economics, Working Paper Series qt45f996hh, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    4. Bhat, Chandra R. & Castelar, Saul, 2002. "A unified mixed logit framework for modeling revealed and stated preferences: formulation and application to congestion pricing analysis in the San Francisco Bay area," Transportation Research Part B: Methodological, Elsevier, vol. 36(7), pages 593-616, August.
    5. 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.
    6. William Greene, 2001. "Fixed and Random Effects in Nonlinear Models," Working Papers 01-01, New York University, Leonard N. Stern School of Business, Department of Economics.
    7. Brownstone, 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 qt45f996hh, University of California Transportation Center.
    8. Brownston, David & Bunch, David S. & Train, Kenneth, 1999. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," Department of Economics, Working Paper Series qt7rf7s3nx, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    9. 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.
    10. Chandra R. Bhat, 2000. "Incorporating Observed and Unobserved Heterogeneity in Urban Work Travel Mode Choice Modeling," Transportation Science, INFORMS, vol. 34(2), pages 228-238, May.
    11. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
    12. Xiaodong Gong & Arthur van Soest, 2002. "Family Structure and Female Labor Supply in Mexico City," Journal of Human Resources, University of Wisconsin Press, vol. 37(1), pages 163-191.
    13. Domanski, Adam, 2009. "Estimating Mixed Logit Recreation Demand Models With Large Choice Sets," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49413, Agricultural and Applied Economics Association.
    14. Hajivassiliou, Vassilis A. & Ruud, Paul A., 1986. "Classical estimation methods for LDV models using simulation," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 40, pages 2383-2441, Elsevier.
    15. Hensher, David A., 2008. "Empirical approaches to combining revealed and stated preference data: Some recent developments with reference to urban mode choice," Research in Transportation Economics, Elsevier, vol. 23(1), pages 23-29, January.
    16. Harris, Katherine M. & Keane, Michael P., 1998. "A model of health plan choice:: Inferring preferences and perceptions from a combination of revealed preference and attitudinal data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 131-157, November.
    17. Lee, Lung-Fei, 1997. "Simulated maximum likelihood estimation of dynamic discrete choice statistical models some Monte Carlo results," Journal of Econometrics, Elsevier, vol. 82(1), pages 1-35.
    18. Jason R. Blevins, 2015. "Structural Estimation Of Sequential Games Of Complete Information," Economic Inquiry, Western Economic Association International, vol. 53(2), pages 791-811, April.
    19. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 343-376, December.
    20. Ricardo A. Daziano & Martin Achtnicht, 2014. "Forecasting Adoption of Ultra-Low-Emission Vehicles Using Bayes Estimates of a Multinomial Probit Model and the GHK Simulator," Transportation Science, INFORMS, vol. 48(4), pages 671-683, November.

    More about this item

    Keywords

    Architecture;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cdl:uctcwp:qt3tb6j874. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/itucbus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.