There Is No Aggregate Bias: Why Macro Logit Models Work
AbstractIn this article, we examine the aggregation properties of (nested) logit models to understand their exceptional macro-level performance. The problem of aggregating micro logit models involves integrating nonlinear functions of model parameters over a distribution of consumer heterogeneity. The aggregation problem is analyzed using a mixture of analytic and simulation techniques, with the simulation experiments using actual panel data to calibrate the distribution of heterogeneity. We conclude that the practice of fitting aggregate logit models is theoretically justified under the following three conditions: (1) All consumers are exposed to the same marketing-mix variables, (2) the brands are close substitutes, and (3) the distribution of prices is not concentrated at an extreme value. These conditions are frequently met in store-level scanner data.
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Bibliographic InfoArticle provided by American Statistical Association in its journal Journal of Business and Economic Statistics.
Volume (Year): 9 (1991)
Issue (Month): 1 (January)
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Web page: http://www.amstat.org/publications/jbes/index.cfm?fuseaction=main
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- Goddard, Ellen W. & Shank, Benjamin & Panter, Chris & Nilsson, Tomas K.H. & Cash, Sean B., 2007. "Canadian Chicken Industry: Consumer Preferences, Industry Structure and Producer Benefits from Investment in Research and Advertising," Project Report Series 52088, University of Alberta, Department of Resource Economics and Environmental Sociology.
- Mercedes Esteban Bravo & José Manuel Vidal-Sanz, 2013. "A nonlinear product differentiation model à la Cournot: a new look to the newspapers industry," Business Economics Working Papers wb132002, Universidad Carlos III, Departamento de Economía de la Empresa.
- Cohen, Michael, 2010. "A Structured Covariance Probit Demand Model," Research Reports 149970, University of Connecticut, Food Marketing Policy Center.
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