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

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  • Patrick Bajari
  • C. Lanier Benkard
Abstract
June 2001 We study the identification and estimation of preferences in hedonic discrete choice models of demand for differentiated products. In the hedonic discrete choice model, products are represented as a finite dimensional bundle of characteristics, and consumers maximize utility subject to a budget constraint. Our hedonic model also incorporates product characteristics that are observed by consumers but not by the economist. We demonstrate that, unlike the case where all product characteristics are observed, it is not in general possible to uniquely recover consumer preferences from data on a consumer's choices. However, we provide several sets of assumptions under which preferences can be recovered uniquely, that we think may be satisfied in many applications. Our identification and estimation strategy is a two stage approach in the spirit of Rosen (1974). In the first stage, we show under some weak conditions that price data can be used to nonparametrically recover the unobserved product characteristics and the hedonic pricing function. In the second stage, we show under some weak conditions that if the product space is continuous and the functional form of utility is known, then there exists an inversion between a consumer's choices and her preference parameters. If the product space is discrete, we propose a Gibbs sampling algorithm to simulate the population distribution of consumers' taste coefficients. Working Papers Index

Suggested Citation

  • Patrick Bajari & C. Lanier Benkard, 2001. "Demand Estimation With Heterogeneous Consumers and Unobserved Product Characteristics: A Hedonic Approach," Working Papers 01010, Stanford University, Department of Economics.
    • Handle: RePEc:wop:stanec:01010
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    Cited by:

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    2. Daniel A. Ackerberg & Marc Rysman, 2005. "Unobserved Product Differentiation in Discrete-Choice Models: Estimating Price Elasticities and Welfare Effects," RAND Journal of Economics, The RAND Corporation, vol. 36(4), pages 771-788, Winter.
    3. Guido W. Imbens & Whitney K. Newey, 2009. "Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity," Econometrica, Econometric Society, vol. 77(5), pages 1481-1512, September.
    4. Rosa L. Matzkin, 2003. "Nonparametric Estimation of Nonadditive Random Functions," Econometrica, Econometric Society, vol. 71(5), pages 1339-1375, September.
    5. Han Hong & Matthew Shum, 2000. "A Semiparametric Estimator for Dynamic Optimization Models," Economics Working Paper Archive 461, The Johns Hopkins University,Department of Economics, revised Nov 2001.
    6. Rosa L. Matzkin & James Heckman & Lars Nesheim, 2002. "Nonparametric Estimation and Nonadditive Hedonic Models," Working Papers 51, Universidad de San Andres, Departamento de Economia, revised Jun 2002.
    7. Bayer, Patrick & Keohane, Nathaniel & Timmins, Christopher, 2009. "Migration and hedonic valuation: The case of air quality," Journal of Environmental Economics and Management, Elsevier, vol. 58(1), pages 1-14, July.
    8. Astrid Mastenbroek & Irma Sirutyte & Robert Sparrow, 2021. "Information Barriers to Adoption of Agricultural Technologies: Willingness to Pay for Certified Seed of an Open Pollinated Maize Variety in Northern Uganda," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(1), pages 180-201, February.

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