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On Asymptotic Properties Of The Parameters Of Differentiated Product Demand And Supply Systems When Demographically Categorized Purchasing Pattern Data Are Available

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  • Satoshi Myojo
  • Yuichiro Kanazawa

Abstract

We derive asymptotic properties of an estimator for supply and demand models extended with additional moments relating consumer demographics to the characteristics of purchased products. We clarify the structure of a practical sampling scheme in which the extended estimator is consistent, asymptotically normal, and more efficient than the original estimator. We provide conditions guaranteeing the asymptotic theorems hold for the random coefficient logit model of demand with oligopolistic suppliers. Extensive simulation studies demonstrate significant benefits of the additional moments in estimating the random coefficient logit model.

Suggested Citation

  • Satoshi Myojo & Yuichiro Kanazawa, 2012. "On Asymptotic Properties Of The Parameters Of Differentiated Product Demand And Supply Systems When Demographically Categorized Purchasing Pattern Data Are Available," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 887-938, August.
  • Handle: RePEc:wly:iecrev:v:53:y:2012:i:3:p:887-938
    DOI: 10.1111/j.1468-2354.2012.00705.x
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    1. K. Sudhir, 2001. "Competitive Pricing Behavior in the Auto Market: A Structural Analysis," Marketing Science, INFORMS, vol. 20(1), pages 42-60, January.
    2. Amil Petrin, 2002. "Quantifying the Benefits of New Products: The Case of the Minivan," Journal of Political Economy, University of Chicago Press, vol. 110(4), pages 705-729, August.
    3. Guido W. Imbens & Tony Lancaster, 1994. "Combining Micro and Macro Data in Microeconometric Models," Review of Economic Studies, Oxford University Press, vol. 61(4), pages 655-680.
    4. Steven T. Berry & Philip A. Haile, 2014. "Identification in Differentiated Products Markets Using Market Level Data," Econometrica, Econometric Society, vol. 82(5), pages 1749-1797, September.
    5. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    6. K. Sudhir, 2001. "Competitive Pricing Behavior in the US Auto Market: A Structural Analysis," Yale School of Management Working Papers ysm228, Yale School of Management.
    7. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-1057, September.
    8. 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.
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