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Efficient Conjoint Choice Designs in the Presence of Respondent Heterogeneity

  • Jie Yu

    ()

    (Faculty of Business and Economics, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium)

  • Peter Goos

    ()

    (Faculty of Applied Economics, Universiteit Antwerpen, B-2000 Antwerpen, Belgium)

  • Martina Vandebroek

    ()

    (Faculty of Business and Economics and Leuven Statistics Research Centre, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium)

Random effects or mixed logit models are often used to model differences in consumer preferences. Data from choice experiments are needed to estimate the mean vector and the variances of the multivariate heterogeneity distribution involved. In this paper, an efficient algorithm is proposed to construct semi-Bayesian -optimal mixed logit designs that take into account the uncertainty about the mean vector of the distribution. These designs are compared to locally -optimal mixed logit designs, Bayesian and locally -optimal designs for the multinomial logit model and to nearly orthogonal designs (Sawtooth (CBC)) for a wide range of parameter values. It is found that the semi-Bayesian mixed logit designs outperform the competing designs not only in terms of estimation efficiency but also in terms of prediction accuracy. In particular, it is shown that assuming large prior values for the variance parameters for constructing semi-Bayesian mixed logit designs is most robust against the misspecification of the prior mean vector. In addition, the semi-Bayesian mixed logit designs are compared to the fully Bayesian mixed logit designs, which take also into account the uncertainty about the variances in the heterogeneity distribution and which can be constructed only using prohibitively large computing power. The differences in estimation and prediction accuracy turn out to be rather small in most cases, which indicates that the semi-Bayesian approach is currently the most appropriate one if one needs to estimate mixed logit models.

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File URL: http://dx.doi.org/10.1287/mksc.1080.0386
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Article provided by INFORMS in its journal Marketing Science.

Volume (Year): 28 (2009)
Issue (Month): 1 (01-02)
Pages: 122-135

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Handle: RePEc:inm:ormksc:v:28:y:2009:i:1:p:122-135
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  1. Lan Luo & P. K. Kannan & Brian T. Ratchford, 2007. "New Product Development Under Channel Acceptance," Marketing Science, INFORMS, vol. 26(2), pages 149-163, 03-04.
  2. Train, Kenneth, 2000. "Halton Sequences for Mixed Logit," Department of Economics, Working Paper Series qt6zs694tp, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
  3. 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.
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  5. Huber, Joel & Train, Kenneth, 2000. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Department of Economics, Working Paper Series qt7zm4f51b, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
  6. David Hensher & William Greene, 2003. "The Mixed Logit model: The state of practice," Transportation, Springer, vol. 30(2), pages 133-176, May.
  7. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
  8. Zsolt Sándor & Michel Wedel, 2002. "Profile Construction in Experimental Choice Designs for Mixed Logit Models," Marketing Science, INFORMS, vol. 21(4), pages 455-475, February.
  9. Neeraj Arora & Ty Henderson, 2007. "Embedded Premium Promotion: Why It Works and How to Make It More Effective," Marketing Science, INFORMS, vol. 26(4), pages 514-531, 07-08.
  10. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
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