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Accounting for Primary and Secondary Demand Effects with Aggregate Data

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Author Info
Nair, Harikesh S. (Stanford U)
Dube, Jean-Pierre (U of Chicago)
Chintagunta, Pradeep

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Abstract

Discrete choice models of aggregate demand, such as the random coefficients logit, can handle large differentiated products categories parsimoniously while still providing flexible substitution patterns. However, the discrete choice assumption may not be appropriate for many categories in which we expect consumers may purchase more than one unit of the selected item. We derive the aggregate demand system corresponding to a discrete/continuous household-level model of demand. We also propose a Method-of-Simulated-Moments procedure that provides consistent estimates of the structural parameters when only aggregate data are available. The procedure also enables the researcher to control both for the potential endogeneity of marketing variables as well as potential heterogeneity in consumer tastes. Using our aggregate estimates, we can measure the decomposition of price elasticities into incidence, brand choice and purchase quantity components. We also propose several empirical tests to assess the validity of the discrete/continuous demand system versus the logit model. In several simulation experiments, we demonstrate the robustness of this model across datasets in which quantity choices may or may not be important. Our empirical calibration to store-level data in the refrigerated orange juice category indicates a considerable improvement in fit of the observed aggregate sales using the discrete/continuous model.

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Paper provided by Stanford University, Graduate School of Business in its series Research Papers with number 1949.

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Date of creation: Jul 2004
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Handle: RePEc:ecl:stabus:1949

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  1. Hanemann, W Michael, 1984. "Discrete-Continuous Models of Consumer Demand," Econometrica, Econometric Society, vol. 52(3), pages 541-61, May. [Downloadable!] (restricted)
  2. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August. [Downloadable!] (restricted)
  3. Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 92(1), pages 1-45, September. [Downloadable!] (restricted)
  4. Steven T. Berry, 1994. "Estimating Discrete-Choice Models of Product Differentiation," RAND Journal of Economics, The RAND Corporation, vol. 25(2), pages 242-262, Summer. [Downloadable!] (restricted)
  5. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-57, September. [Downloadable!] (restricted)
  6. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-90, July. [Downloadable!] (restricted)
  7. Heerde, H.J. van & Gupta, S. & Wittink, D.R., 2003. "Is 3/4 of the sales promotion bump due to brand switching? No it is 1/3," Discussion Paper 5, Tilburg University, Center for Economic Research. [Downloadable!]
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  1. Ching-I Huang, 2008. "Estimating demand for cellular phone service under nonlinear pricing," Quantitative Marketing and Economics, Springer, vol. 6(4), pages 371-413, December. [Downloadable!] (restricted)
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  2. Sridhar Narayanan & Pradeep Chintagunta & Eugenio Miravete, 2007. "The role of self selection, usage uncertainty and learning in the demand for local telephone service," Quantitative Marketing and Economics, Springer, vol. 5(1), pages 1-34, March. [Downloadable!] (restricted)
  3. Richards, Timothy J. & Acharya, Ram & Molina, Ignacio, 2009. "Retail and Wholesale Market Power in Organic Foods," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49329, Agricultural and Applied Economics Association. [Downloadable!]
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