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Stochastic Prediction in Multinomial Logit Models

Author

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  • Arthur Hsu

    (Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Ronald T. Wilcox

    (Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

It is standard practice to form predictions from multinomial logit models by ignoring the estimation error associated with the parameter estimates and solving for the predicted endogeneous variable (market share) in terms of the exogenous variables and the point estimates of the parameters. It has long been recognized in the econometrics literature that this type of nonstochastic prediction, which ignores the sampling distribution of the parameter estimates, leads to incorrect inferences about the endogenous variable. We offer a simulationbased approach for approximating the exact stochastic prediction. We show that this approach provides very accurate approximations with minimal computation time and would be easy to implement in industrial applications.

Suggested Citation

  • Arthur Hsu & Ronald T. Wilcox, 2000. "Stochastic Prediction in Multinomial Logit Models," Management Science, INFORMS, vol. 46(8), pages 1137-1144, August.
    • Handle: RePEc:inm:ormnsc:v:46:y:2000:i:8:p:1137-1144
    DOI: 10.1287/mnsc.46.8.1137.12028
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    References listed on IDEAS

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    Cited by:

    1. Thomas J. Steenburgh & Andrew Ainslie & Peder Hans Engebretson, 2003. "Massively Categorical Variables: Revealing the Information in Zip Codes," Marketing Science, INFORMS, vol. 22(1), pages 40-57, August.
    2. T H Moon & S Y Sohn, 2011. "Survival analysis for technology credit scoring adjusting total perception," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1159-1168, June.
    3. Moon, Tae Hee & Sohn, So Young, 2008. "Technology scoring model for reflecting evaluator's perception within confidence limits," European Journal of Operational Research, Elsevier, vol. 184(3), pages 981-989, February.

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