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Forecasting new product penetration with flexible substitution patterns

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  • 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.

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Bibliographic Info

Paper provided by University of California Transportation Center in its series University of California Transportation Center, Working Papers with number qt1j6814b3.

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Date of creation: 01 Jan 1999
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Handle: RePEc:cdl:uctcwp:qt1j6814b3

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Related research

Keywords: Mixed logit; Random-coefficients logit; Probit; Simulation; Social and Behavioral Sciences;

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References

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  1. Daniel McFadden, 1987. "A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration," Working papers, Massachusetts Institute of Technology (MIT), Department of Economics 464, Massachusetts Institute of Technology (MIT), Department of Economics.
  2. 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, University of California Transportation Center qt3sm7w9zk, University of California Transportation Center.
  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, University of California Transportation Center qt0244r8g2, University of California Transportation Center.
  4. Lee, L-F., 1990. "On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models," Papers, Minnesota - Center for Economic Research 260, Minnesota - Center for Economic Research.
  5. Kenneth E. Train, 1998. "Recreation Demand Models with Taste Differences over People," Land Economics, University of Wisconsin Press, University of Wisconsin Press, vol. 74(2), pages 230-239.
  6. Train, Kenneth E., 1995. "Simulation Methods for Probit and Related Models Based on Convenient Error Partitioning," Department of Economics, Working Paper Series, Department of Economics, Institute for Business and Economic Research, UC Berkeley qt94h8x4gd, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
  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, MIT Press, vol. 80(4), pages 647-657, November.
  8. McFadden, Daniel & Ruud, Paul A, 1994. "Estimation by Simulation," The Review of Economics and Statistics, MIT Press, MIT Press, vol. 76(4), pages 591-608, November.
  9. Tülin Erdem, 1996. "A Dynamic Analysis of Market Structure Based on Panel Data," Marketing Science, INFORMS, INFORMS, vol. 15(4), pages 359-378.
  10. Stern, Steven, 1992. "A Method for Smoothing Simulated Moments of Discrete Probabilities in Multinomial Probit Models," Econometrica, Econometric Society, Econometric Society, vol. 60(4), pages 943-52, July.
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