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

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Author Info
Steven Tenn ()
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

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File URL: http://hdl.handle.net/10.1007/s11129-006-9011-3
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Publisher Info
Article provided by Springer in its journal Quantitative Marketing and Economics.

Volume (Year): 4 (2006)
Issue (Month): 4 (December)
Pages: 383-405
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:kap:qmktec:v:4:y:2006:i:4:p:383-405

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Web page: http://www.springerlink.com/link.asp?id=111240

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Related research
Keywords: Aggregation bias; Demand estimation; Scanner data;

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

  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.
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  2. Hausman, Jerry A & Leonard, Gregory K, 2002. "The Competitive Effects of a New Product Introduction: A Case Study," Journal of Industrial Economics, Blackwell Publishing, vol. 50(3), pages 237-63, September. [Downloadable!] (restricted)
  3. Chung, Chanjin & Kaiser, Harry M, 2002. " Advertising Evaluation and Cross-Sectional Data Aggregation," American Journal of Agricultural Economics, American Agricultural Economics Association, vol. 84(3), pages 800-806, August. [Downloadable!] (restricted)
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