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Demand Estimation With Heterogeneous Consumers and Unobserved Product Characteristics: A Hedonic Approach

  • Bajari, Patrick

    (Duke U)

  • Benkard, C. Lanier

    (Stanford U)

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    We study the identification and estimation of Gorman-Lancaster style hedonic models of demand for differentiated products for the case when one product characteristic is not observed. Our identification and estimation strategy is a two-step approach in the spirit of Rosen (1974). Relative to Rosen's approach, we generalize the first stage estimation to allow for a single dimensional unobserved product characteristic, and also allow the hedonic pricing function to have a general, non-additive structure. In the second stage, if the product space is continuous and the functional form of utility is known then there exists an inversion between the consumer's choices and her preference parameters. This inversion can be used to recover the distribution of random coeffcients nonparametrically. For the more common case when the set of products is finite, we use the revealed preference conditions from the hedonic model to develop a Gibbs sampling estimator for the distribution of random coeffcients. We apply our methods to estimating personal computer demand.

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    File URL: http://gsbapps.stanford.edu/researchpapers/library/RP1842.pdf
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    Paper provided by Stanford University, Graduate School of Business in its series Research Papers with number 1842.

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    Date of creation: Jan 2004
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    Handle: RePEc:ecl:stabus:1842
    Contact details of provider: Postal: Stanford University, Stanford, CA 94305-5015
    Phone: (650) 723-2146
    Fax: (650)725-6750
    Web page: http://gsbapps.stanford.edu/researchpapers/
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    8. Geweke, John & Keane, Michael P & Runkle, David, 1994. "Alternative Computational Approaches to Inference in the Multinomial Probit Model," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 609-32, November.
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    12. Geweke, John, 1996. "Monte carlo simulation and numerical integration," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 15, pages 731-800 Elsevier.
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    17. Goettler, Ronald L & Shachar, Ron, 2001. "Spatial Competition in the Network Television Industry," RAND Journal of Economics, The RAND Corporation, vol. 32(4), pages 624-56, Winter.
    18. Patrick Bajari & Matthew E. Kahn, 2003. "Estimating Housing Demand with an Application to Explaining Racial Segregation in Cities," NBER Working Papers 9891, National Bureau of Economic Research, Inc.
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