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Avoiding aggregation bias in demand estimation: A multivariate promotional disaggregation approach

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  • Steven Tenn

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Abstract

Demand models produce biased results when applied to data aggregated across stores with heterogeneous promotional activity. We show how to modify extant aggregate demand frameworks to avoid this problem. First a consumer-level model is developed, which is then integrated over the heterogeneous stores to arrive at aggregate demand. Our approach is highly practical since it requires only standard scanner data of the type produced by the major vendors. Using data for super-premium ice cream, we apply the proposed methodology to the random coefficients logit demand framework. Copyright Springer Science + Business Media, LLC 2006

Suggested Citation

  • Steven Tenn, 2006. "Avoiding aggregation bias in demand estimation: A multivariate promotional disaggregation approach," Quantitative Marketing and Economics (QME), Springer, vol. 4(4), pages 383-405, December.
  • Handle: RePEc:kap:qmktec:v:4:y:2006:i:4:p:383-405
    DOI: 10.1007/s11129-006-9011-3
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    File URL: http://hdl.handle.net/10.1007/s11129-006-9011-3
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    References listed on IDEAS

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    1. Daniel A. Ackerberg & Marc Rysman, 2005. "Unobserved Product Differentiation in Discrete-Choice Models: Estimating Price Elasticities and Welfare Effects," RAND Journal of Economics, The RAND Corporation, vol. 36(4), pages 771-788, Winter.
    2. Hausman, Jerry A & Leonard, Gregory K, 2002. "The Competitive Effects of a New Product Introduction: A Case Study," Journal of Industrial Economics, Wiley Blackwell, vol. 50(3), pages 237-263, September.
    3. Aviv Nevo, 2000. "Mergers with Differentiated Products: The Case of the Ready-to-Eat Cereal Industry," RAND Journal of Economics, The RAND Corporation, vol. 31(3), pages 395-421, Autumn.
    4. Peter Boatwright & Sanjay Dhar & Peter Rossi, 2004. "The Role of Retail Competition, Demographics and Account Retail Strategy as Drivers of Promotional Sensitivity," Quantitative Marketing and Economics (QME), Springer, vol. 2(2), pages 169-190, June.
    5. Allenby, G.M. & Rossi, P.E., 1988. "There Is No Aggregation Bias: Why Macro Logit Models Work," Papers 88-62, Chicago - Graduate School of Business.
    6. Arthur Lewbel, 1992. "Aggregation with Log-Linear Models," Review of Economic Studies, Oxford University Press, vol. 59(3), pages 635-642.
    7. Randolph E. Bucklin & Sunil Gupta, 1999. "Commercial Use of UPC Scanner Data: Industry and Academic Perspectives," Marketing Science, INFORMS, vol. 18(3), pages 247-273.
    8. Chanjin Chung & Harry M. Kaiser, 2002. "Advertising Evaluation and Cross-Sectional Data Aggregation," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(3), pages 800-806.
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    Cited by:

    1. Tenn, Steven & Froeb, Luke & Tschantz, Steven, 2010. "Mergers when firms compete by choosing both price and promotion," International Journal of Industrial Organization, Elsevier, vol. 28(6), pages 695-707, November.
    2. Hernán A. Bruno & Naufel J. Vilcassim, 2008. "—Structural Demand Estimation with Varying Product Availability," Marketing Science, INFORMS, vol. 27(6), pages 1126-1131, 11-12.
    3. Draganska, Michaela & Klapper, Daniel, 2010. "Choice Set Heterogeneity and the Role of Advertising: An Analysis with Micro and Macro Data," Research Papers 2063, Stanford University, Graduate School of Business.
    4. Tenn, Steven & Yun, John M., 2008. "Biases in demand analysis due to variation in retail distribution," International Journal of Industrial Organization, Elsevier, vol. 26(4), pages 984-997, July.

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