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A Proposal to Distinguish State Dependence and Unobserved Heterogeneity in Binary Brand Choice Models

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  • José M. Labeaga
  • Mercedes Martos-Partal

Abstract

This paper uses binary choice models that specify four possible sources of observed regularity in the consumer brand choice decision over purchase occasion: namely, state dependence, observed and unobserved heterogeneity and correlation effects. The objective is to distinguish correctly among the effects of these four variables. The estimation method proposed is an alternative to the most commonly used estimation methods in marketing choice models. We consider that the alternative method appropriately controls for observed heterogeneity and unobserved heterogeneity correlated with the state dependence variable because of the way the state dependence variable is built. The model is used for the first time in marketing following the methodology proposed by Chamberlain (1984). A relationship for unobserved heterogeneity is specified, taking into account the correlation among unobserved heterogeneity and other choice determinants. In this way, we split the influence of household state dependence and tastes on brand choice. The findings are very conclusive. We find that because the individual effects and the covariates are correlated, traditional estimation methods cannot be used to split state dependence and unobserved heterogeneity. The proposed model is found to yield better measures of predictive performance than the conventional model. The results are found to be robust across categories of laundry detergent and have significant implications for marketing policy.

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Bibliographic Info

Paper provided by FEDEA in its series Working Papers with number 2007-02.

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Date of creation: Feb 2007
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Handle: RePEc:fda:fdaddt:2007-02

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  1. Erdem, Tulin & Sun, Baohong, 2001. "Testing for Choice Dynamics in Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 142-52, April.
  2. Elrod, Terry & Keane, Michael, 1995. "A Factor-Analytic Probit Model for Representing the Market Structure in Panel Data," MPRA Paper 52434, University Library of Munich, Germany.
  3. P. B. Seetharaman, 2004. "Modeling Multiple Sources of State Dependence in Random Utility Models: A Distributed Lag Approach," Marketing Science, INFORMS, vol. 23(2), pages 263-271, April.
  4. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318 Elsevier.
  5. James J. Heckman, 1981. "Heterogeneity and State Dependence," NBER Chapters, in: Studies in Labor Markets, pages 91-140 National Bureau of Economic Research, Inc.
  6. Lakshman Krishnamurthi & S. P. Raj, 1988. "A Model of Brand Choice and Purchase Quantity Price Sensitivities," Marketing Science, INFORMS, vol. 7(1), pages 1-20.
  7. J. Morgan Jones & Jane T. Landwehr, 1988. "Removing Heterogeneity Bias from Logit Model Estimation," Marketing Science, INFORMS, vol. 7(1), pages 41-59.
  8. 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 310-27, July.
  9. Tülin Erdem, 1996. "A Dynamic Analysis of Market Structure Based on Panel Data," Marketing Science, INFORMS, vol. 15(4), pages 359-378.
  10. 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.
  11. Andrew M. Jones & José M. Labeaga, 2003. "Individual heterogeneity and censoring in panel data estimates of tobacco expenditure," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(2), pages 157-177.
  12. 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.
  13. Füsun Gönül & Kannan Srinivasan, 1993. "Modeling Multiple Sources of Heterogeneity in Multinomial Logit Models: Methodological and Managerial Issues," Marketing Science, INFORMS, vol. 12(3), pages 213-229.
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