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An anti-ideal point representation of economic discrete choice models

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  • Bordley, Robert F.

Abstract

The popular mixed logit model, which can closely approximate virtually any other random utility model can be approximated by a more visual, and computationally more tractable, anti-ideal point model.

Suggested Citation

  • Bordley, Robert F., 2011. "An anti-ideal point representation of economic discrete choice models," Economics Letters, Elsevier, vol. 110(1), pages 60-63, January.
    • Handle: RePEc:eee:ecolet:v:110:y:2011:i:1:p:60-63
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    References listed on IDEAS

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    1. Wagner A. Kamakura & Rajendra K. Srivastava, 1986. "An Ideal-Point Probabilistic Choice Model for Heterogeneous Preferences," Marketing Science, INFORMS, vol. 5(3), pages 199-218.
    2. Brownstone, David & Train, Kenneth, 1999. "Forecasting new product penetration with flexible substitution patterns," Department of Economics, Working Paper Series qt1j6814b3, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    3. Brownstone, David & Train, Kenneth, 1999. "Forecasting new product penetration with flexible substitution patterns," Department of Economics, Working Paper Series qt3tb6j874, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    4. Hansen, Eric R., 1987. "Industrial location choice in Sao Paulo, Brazil : A nested logit model," Regional Science and Urban Economics, Elsevier, vol. 17(1), pages 89-108, February.
    5. John M. Quigley, 1976. "Housing Demand in the Short Run: An Analysis of Polytomous Choice," NBER Chapters, in: Explorations in Economic Research, Volume 3, number 1, pages 76-102, National Bureau of Economic Research, Inc.
    6. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    7. 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.
    8. Matthew C. Harding & Jerry Hausman, 2007. "Using A Laplace Approximation To Estimate The Random Coefficients Logit Model By Nonlinear Least Squares," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1311-1328, November.
    9. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, November.
    10. Mark Bagnoli & Ted Bergstrom, 2006. "Log-concave probability and its applications," Studies in Economic Theory, in: Charalambos D. Aliprantis & Rosa L. Matzkin & Daniel L. McFadden & James C. Moore & Nicholas C. Yann (ed.), Rationality and Equilibrium, pages 217-241, Springer.
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    Cited by:

    1. Junghyun Park & Minki Kim & Pradeep K Chintagunta, 2022. "Mapping Consumers’ Context-Dependent Consumption Preferences: A Multidimensional Unfolding Approach [An Empirical Comparison of Logit Choice Models with Discrete versus Continuous Representations of Heterogeneity]," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 49(2), pages 202-228.
    2. Ardakani, Omid M. & Bordley, Robert F. & Soofi, Ehsan S., 2025. "Expected information of noisy attribute forecasts for probabilistic forecasts," European Journal of Operational Research, Elsevier, vol. 323(3), pages 1013-1023.

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