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Marketing Letters

, Volume 6, Issue 2, pp 159–169 | Cite as

Incorporating heterogeneity with store-level aggregate data

  • Byung-Do Kim
Article

Abstract

This paper introduces a methodology to incorporate heterogeneity in the analysis of store level aggregate data. The proposed model is validated using two sets of scanner panel data, for tuna and ketchup, and their corresponding weekly aggregate data. The model recovers the true parameters with acceptable accuracy.

The model has several advantages over the previous aggregate models, such as the linear model, the semilog model, and the log-log model. First, the cross-price elasticities estimated from the model show the asymmetric responses to the price promotions very close to those from the logit model applied to the panel data. Second, the model shows better prediction performance.

Key words

Heterogeneity aggregation bias random coefficient models 

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References

  1. Allenby, G.M., and P.E. Rossi. (1991). “There Is No Aggregation Bias: Why Macro Logit Models Work.”Journal of Business & Economic Statistics, 9(1), 1–14.Google Scholar
  2. Blattberg, R.C., and K.J. Wisniewski. (1989). “Price-Induced Patterns of Competition.”Marketing Science, 8(4), 291–309.Google Scholar
  3. Chintagunta, P.K., D.C. Jain, and N.J. Vilcassim. (1991). “Investigating Heterogeneity in Brand Preferences in Logit Models for Panel Data.”Journal of Marketing Research, 28(4), 417–428.Google Scholar
  4. Davis, P., and P. Rabinowitz. (1984).Methods of Numerical Integration. Orlando: Academic Press.Google Scholar
  5. Fader, P.S., and J.M. Lattin (1993). “Accounting for Heterogeneity and Nonstationarity in a Cross-Sectional Model of Consumer Purchase Behavior.”Marketing Science, 12(3), 304–307.Google Scholar
  6. Gonul, F., and K. Srinivasan. (1993). “Modeling Unobserved Heterogeneity in Multinational Logit Models: Methodological and Managerial Issues.”Marketing Science, 12(3), 213–229.Google Scholar
  7. Guadagni, P.M., and J.D.C. Little. (1983). “A Logit Model of Brand Choice Calibrated on Scanner Data.”Marketing Science, 2(3), 203–238.Google Scholar
  8. Hsiao, C. (1986).Analysis of Panel Data. Cambridge: Cambridge University Press.Google Scholar
  9. Jones, J.M., and J.T. Landwehr. (1988). “Removing Heterogeneity Bias from Logit Model Estimation.”Marketing Science, 7(1), 41–59.Google Scholar
  10. Kamakura, W.A., and G.J. Russell. (1989). “A Probabilistic Choice Model for Market Segmentation and Elasticity Structure.”Journal of Marketing Research, 26(4) 379–390.Google Scholar
  11. Kim, B., R.C. Blattberg, and P.E. Rossi. (1995). “Modelling the Distribution of Price Sensitivity and Implications for Optimal Retail Pricing.” forthcoming.Journal of Business and Economic Statistics.Google Scholar
  12. Stoker, T. (1983). “Completeness, Distribution Restrictions, and the Form of Aggregate Functions.”Econometrica, 52(4) 887–907.Google Scholar
  13. Stoker, T. (1993). “Empirical Approaches to the Problem of Aggregation Over Individuals.”Journal of Economic Literature, 31, 1827–1874.Google Scholar

Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Byung-Do Kim
    • 1
  1. 1.Graduate School of Industrial AdministrationCarnegie Mellon UniversityPittsburgh

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