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Demand Estimation With Heterogeneous Consumers and Unobserved Product Characteristics: A Hedonic Approach Author info | Abstract | Publisher info | Download info | Related research | Statistics Bajari, Patrick (Duke U)
Benkard, C. Lanier (Stanford U)
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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Paper provided by Stanford University, Graduate School of Business in its series Research Papers with number
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Date of creation: Jan 2004Date of revision:
Handle: RePEc:ecl:stabus:1842Contact details of provider: Postal: Stanford University, Stanford, CA 94305-5015 Phone: (650) 723-2146 Fax: (650)725-6750 Email: Web page: http://gsbapps.stanford.edu/researchpapers/ More information through EDIRC
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Christos Genakos, 2004.
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Bajari, Patrick & Benkard, C. Lanier & Levin, Jonathan, 2007.
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Laura Blow & Martin Browning & Ian Crawford, 2005.
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