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Estimating a Multinomial Probit Model of Brand Choice Using the Method of Simulated Moments

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  • Pradeep K. Chintagunta

    (Cornell University)

Abstract

The multinomial probit model of brand choice is theoretically appealing for marketing applications as it is free from the “independence of irrelevant alternatives” property of the multinomial logit model. However, difficulties in estimation have restricted its widespread use in marketing. This paper presents an application of the method of simulated moments, a new methodology that enables easy estimation of probit models with a large number of alternatives in the choice set. We describe the theoretical development of the technique and using pseudo-simulated data, conduct numerical experiments to compare the method with existing techniques for estimating probit models. Using the scanner panel data on the purchases of catsup, we provide an empirical application of the method of simulated moments to the estimation of the parameters of a multinomial probit model. Estimating the covariance structure associated with the underlying latent variable probit model enables us to identify broad patterns of similarities across alternatives. It also enables us to derive a pairwise similarity matrix across choice alternatives which when input into a multi-dimensional scaling routine provides us with a graphical representation of competitive structure in the catsup market. For completeness, we compare the substantive implications for the effects of marketing variables obtained from the multinomial probit model with those obtained from models in the extant marketing literature.

Suggested Citation

  • Pradeep K. Chintagunta, 1992. "Estimating a Multinomial Probit Model of Brand Choice Using the Method of Simulated Moments," Marketing Science, INFORMS, vol. 11(4), pages 386-407.
  • Handle: RePEc:inm:ormksc:v:11:y:1992:i:4:p:386-407
    DOI: 10.1287/mksc.11.4.386
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    Cited by:

    1. Boztuğ, Yasemin & Hildebrandt, Lutz, 1998. "Nicht- und semiparametrische Markenwahlmodelle im Marketing," SFB 373 Discussion Papers 1998,99, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Shao, Wei & Lye, Ashley & Rundle-Thiele, Sharyn, 2009. "Different strokes for different folks: A method to accommodate decision -making heterogeneity," Journal of Retailing and Consumer Services, Elsevier, vol. 16(6), pages 495-501.
    3. Hruschka, Harald & Fettes, Werner & Probst, Markus, 2004. "An empirical comparison of the validity of a neural net based multinomial logit choice model to alternative model specifications," European Journal of Operational Research, Elsevier, vol. 159(1), pages 166-180, November.
    4. Can, Vo Van, 2013. "Estimation of travel mode choice for domestic tourists to Nha Trang using the multinomial probit model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 149-159.
    5. Baltas, George & Doyle, Peter, 2001. "Random utility models in marketing research: a survey," Journal of Business Research, Elsevier, vol. 51(2), pages 115-125, February.
    6. Ma, Li-Ching, 2010. "Visualizing preferences on spheres for group decisions based on multiplicative preference relations," European Journal of Operational Research, Elsevier, vol. 203(1), pages 176-184, May.
    7. Olga V. Kotomina, 2015. "Spatial Dimension of Knowledge Intensive Business Services in Russia," HSE Working papers WP BRP 50/STI/2015, National Research University Higher School of Economics.
    8. Deng, Yiting & Staelin, Richard & Wang, Wei & Boulding, William, 2018. "Consumer sophistication, word-of-mouth and “False” promotions," Journal of Economic Behavior & Organization, Elsevier, vol. 152(C), pages 98-123.
    9. Manrai, Ajay K., 1995. "Mathematical models of brand choice behavior," European Journal of Operational Research, Elsevier, vol. 82(1), pages 1-17, April.

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