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Modeling preference evolution in discrete choice models: A Bayesian state-space approach

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  • Mohamed Lachaab
  • Asim Ansari
  • Kamel Jedidi
  • Abdelwahed Trabelsi

Abstract

We develop discrete choice models that account for parameter driven preference dynamics. Choice model parameters may change over time because of shifting market conditions or due to changes in attribute levels over time or because of consumer learning. In this paper we show how such preference evolution can be modeled using hierarchial Bayesian state space models of discrete choice. The main feature of our approach is that it allows for the simultaneous incorporation of multiple sources of preference and choice dynamics. We show how the state space approach can include state dependence, unobserved heterogeneity, and more importantly, temporal variability in preferences using a correlated sequence of population distributions. The proposed model is very general and nests commonly used choice models in the literature as special cases. We use Markov chain monte carlo methods for estimating model parameters and apply our methodology to a scanner data set containing household brand choices over an eight-year period. Our analysis indicates that preferences exhibit significant variation over the time-span of the data and that incorporating time-variation in parameters is crucial for appropriate inferences regarding the magnitude and evolution of choice elasticities. We also find that models that ignore time variation in parameters can yield misleading inferences about the impact of causal variables. Copyright Springer Science + Business Media, LLC 2006

Suggested Citation

  • Mohamed Lachaab & Asim Ansari & Kamel Jedidi & Abdelwahed Trabelsi, 2006. "Modeling preference evolution in discrete choice models: A Bayesian state-space approach," Quantitative Marketing and Economics (QME), Springer, vol. 4(1), pages 57-81, March.
  • Handle: RePEc:kap:qmktec:v:4:y:2006:i:1:p:57-81
    DOI: 10.1007/s11129-006-6559-x
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    1. Hausman, Jerry A & Wise, David A, 1978. "A Conditional Probit Model for Qualitative Choice: Discrete Decisions Recognizing Interdependence and Heterogeneous Preferences," Econometrica, Econometric Society, vol. 46(2), pages 403-426, March.
    2. McCulloch, Robert E. & Polson, Nicholas G. & Rossi, Peter E., 2000. "A Bayesian analysis of the multinomial probit model with fully identified parameters," Journal of Econometrics, Elsevier, vol. 99(1), pages 173-193, November.
    3. Rishin Roy & Pradeep K. Chintagunta & Sudeep Haldar, 1996. "A Framework for Investigating Habits, “The Hand of the Past,” and Heterogeneity in Dynamic Brand Choice," Marketing Science, INFORMS, vol. 15(3), pages 280-299.
    4. Keane, Michael P, 1997. "Modeling Heterogeneity and State Dependence in Consumer Choice Behavior," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 310-327, July.
    5. Robert E. McCulloch & Ruey S. Tsay, 1994. "Statistical Analysis Of Economic Time Series Via Markov Switching Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(5), pages 523-539, September.
    6. Tülin Erdem, 1996. "A Dynamic Analysis of Market Structure Based on Panel Data," Marketing Science, INFORMS, vol. 15(4), pages 359-378.
    7. Marnik G. Dekimpe & Dominique M. Hanssens, 1995. "The Persistence of Marketing Effects on Sales," Marketing Science, INFORMS, vol. 14(1), pages 1-21.
    8. McCulloch, Robert E. & Rossi, Peter E., 2000. "Reply to Nobile," Journal of Econometrics, Elsevier, vol. 99(2), pages 347-348, December.
    9. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
    10. Geweke, John F. & Keane, Michael P. & Runkle, David E., 1997. "Statistical inference in the multinomial multiperiod probit model," Journal of Econometrics, Elsevier, vol. 80(1), pages 125-165, September.
    11. Nobile, Agostino, 2000. "Comment: Bayesian multinomial probit models with a normalization constraint," Journal of Econometrics, Elsevier, vol. 99(2), pages 335-345, December.
    12. Allenby, Greg M & Lenk, Peter J, 1995. "Reassessing Brand Loyalty, Price Sensitivity, and Merchandising Effects on Consumer Brand Choice," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 281-289, July.
    13. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    14. Sha Yang & Yuxin Chen & Greg Allenby, 2003. "Bayesian Analysis of Simultaneous Demand and Supply," Quantitative Marketing and Economics (QME), Springer, vol. 1(3), pages 251-275, September.
    15. Allenby, Greg M. & Rossi, Peter E., 1998. "Marketing models of consumer heterogeneity," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 57-78, November.
    16. Pradeep K. Chintagunta, 1993. "Investigating Purchase Incidence, Brand Choice and Purchase Quantity Decisions of Households," Marketing Science, INFORMS, vol. 12(2), pages 184-208.
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