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Simulation Methods for Probit and Related Models Based on Convenient Error Partitioning

Author

Listed:
  • Kenneth E. Train.

Abstract

Two probit simulators are described that are conceptually and computationally simple. The first is based on simulating the utilities of the non-chosen alternatives and calculating the probability that the chosen alternative's utility exceeds this maximum. This simulator is apparently new. The second, which is implicit in the discussions of McFadden (1989) and Bolduc (1992), is applicable when the covariance among utilities arises from random parameters and/or error components that are common across alternatives. The parameters and common error components are simulated, and then the probability that the observed event occurs is calculated conditional on these values. Both simulators are unbiased, strictly positive, and continuous. The second is twice-differentiable, while the first has points of non-differentiability. Both are easy to program and can be expected to be very fast computationa- lly.

Suggested Citation

  • Kenneth E. Train., 1995. "Simulation Methods for Probit and Related Models Based on Convenient Error Partitioning," Economics Working Papers 95-237, University of California at Berkeley.
    • Handle: RePEc:ucb:calbwp:95-237
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    Citations

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    Cited by:

    1. 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.
    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. Zhenshan Chen & Stephen K. Swallow & Ian T. Yue, 2020. "Non-participation and Heterogeneity in Stated: A Double Hurdle Latent Class Approach for Climate Change Adaptation Plans and Ecosystem Services," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 77(1), pages 35-67, September.
    4. Weese, Eric, 2011. "Political Mergers as Coalition Formation," Center Discussion Papers 107268, Yale University, Economic Growth Center.
    5. Siros Tongchure, 2013. "Cassava Smallholders’ Participation in Contract Farming in Nakhon Ratchasrima Province, Thailand," Journal of Social and Development Sciences, AMH International, vol. 4(7), pages 332-338.
    6. 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.
    7. Bhat, Chandra R., 2003. "Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 837-855, November.
    8. Brownstone, David, 2001. "Discrete Choice Modeling for Transportation," University of California Transportation Center, Working Papers qt29v7d1pk, University of California Transportation Center.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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