Stochastic Prediction in Multinomial Logit Models

Arthur Hsu () and Ronald T. Wilcox ()
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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

Management Science, 2000, vol. 46, issue 8, 1137-1144

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.

Keywords: choice models; econometric modeling (search for similar items in EconPapers)
Date: 2000
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Citations: View citations in EconPapers (3)

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