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We Know What You Choose! External Validity of Discrete Choice Models


  • R. Karina Gallardo
  • Jaebong Chang

    () (School of Economic Sciences, Washington State University)


For over the last thirty years the multinomial logit model has been the standard in choice modeling. Development in econometrics and computational algorithms has led to the increasing tendency to opt for more flexible models able to depict more realistically choice behavior. This study compares three discrete choice models, the standard multinomial logit, the error components logit, and the random parameters logit. Data were obtained from two choice experiments conducted to investigate consumers’ preferences for fresh pears receiving several postharvest treatments. Model comparisons consisted of in-sample and holdout sample evaluations. Results show that product characteristics hence, datasets, influence model performance. We also found that the multinomial logit model outperformed in at least one of three evaluations in both datasets. Overall, findings signal the need for further studies controlling for context and dataset to have more conclusive cues for discrete choice models capabilities.

Suggested Citation

  • R. Karina Gallardo & Jaebong Chang, 2010. "We Know What You Choose! External Validity of Discrete Choice Models," Working Papers 2009-07, School of Economic Sciences, Washington State University.
  • Handle: RePEc:wsu:wpaper:gallardo-6

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    References listed on IDEAS

    1. Han Bleichrodt & Jose Luis Pinto, 2000. "A Parameter-Free Elicitation of the Probability Weighting Function in Medical Decision Analysis," Management Science, INFORMS, vol. 46(11), pages 1485-1496, November.
    2. Camerer, Colin F., 1998. "Prospect Theory in the Wild: Evidence From the Field," Working Papers 1037, California Institute of Technology, Division of the Humanities and Social Sciences.
    3. Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998. "Misclassification of the dependent variable in a discrete-response setting," Journal of Econometrics, Elsevier, vol. 87(2), pages 239-269, September.
    4. repec:adr:anecst:y:1999:i:55-56:p:09 is not listed on IDEAS
    5. Han, Aaron K., 1987. "Non-parametric analysis of a generalized regression model : The maximum rank correlation estimator," Journal of Econometrics, Elsevier, vol. 35(2-3), pages 303-316, July.
    6. Wakker, Peter & Tversky, Amos, 1993. "An Axiomatization of Cumulative Prospect Theory," Journal of Risk and Uncertainty, Springer, vol. 7(2), pages 147-175, October.
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    More about this item


    discrete choice models; validation; holdout sample;

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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