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Identification and Estimation of Hedonic Models

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  • Ivar Ekeland
  • James J. Heckman
  • Lars Nesheim

Abstract

This paper considers the identification and estimation of hedonic models. We establish that in an additive version of the hedonic model, technology and preferences are generically identified up to affine transformations from data on demand and supply in a single hedonic market. For a very general parametric structure preferences and technology are fully identified. This is true under a strong assumption of statistical independence of the error term. It is also true under the weaker assumption of mean independence of the error term. Much of the confusion in the empircial literature that claims that hedonic models estimated on data from a single market are fundamentally underidentified is based on linearizations that do not use all of the information in the model. The exact economic model that justifies widely used linear approximations has strange properties so the approximation is doubly poor. A semiparametric estimation method is proposed that is valid when a statistical independence assumption is valid. Alternatively, under the weaker condition of mean independence instrumental variables estimators can be applied to identify technology and preference parameters from a single market. They are justified by nonlinearities that are generic features of equilibrium in hedonic models.

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Paper provided by CESifo Group Munich in its series CESifo Working Paper Series with number 1031.

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Date of creation: 2003
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Handle: RePEc:ces:ceswps:_1031

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Keywords: additive hedonic models; semiparametric estimation;

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  1. Helen Tauchen & Ann Dryden Witte, 2001. "Estimating Hedonic Models: Implications of the Theory," NBER Technical Working Papers 0271, National Bureau of Economic Research, Inc.
  2. Kahn, Shulamit & Lang, Kevin, 1988. "Efficient Estimation of Structural Hedonic Systems," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 29(1), pages 157-66, February.
  3. Ivar Ekeland & James J. Heckman & Lars Nesheim, 2004. "Identification and Estimation of Hedonic Models," Journal of Political Economy, University of Chicago Press, vol. 112(S1), pages S60-S109, February.
  4. Epple, Dennis, 1987. "Hedonic Prices and Implicit Markets: Estimating Demand and Supply Functions for Differentiated Products," Journal of Political Economy, University of Chicago Press, vol. 95(1), pages 59-80, February.
  5. James N. Brown & Harvey S. Rosen, 1982. "On the Estimation of Structural Hedonic Price Models," NBER Technical Working Papers 0018, National Bureau of Economic Research, Inc.
  6. Lars Nesheim, 2004. "Equilibrium Sorting of Heterogeneous Consumers Across Locations," Econometric Society 2004 North American Summer Meetings 337, Econometric Society.
  7. Bartik, Timothy J, 1987. "The Estimation of Demand Parameters in Hedonic Price Models," Journal of Political Economy, University of Chicago Press, vol. 95(1), pages 81-88, February.
  8. Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, University of Chicago Press, vol. 82(1), pages 34-55, Jan.-Feb..
  9. Mussa, Michael & Rosen, Sherwin, 1978. "Monopoly and product quality," Journal of Economic Theory, Elsevier, vol. 18(2), pages 301-317, August.
  10. James J. Heckman & Edward J. Vytlacil, 2000. "Local Instrumental Variables," NBER Technical Working Papers 0252, National Bureau of Economic Research, Inc.
  11. Dennis Epple & Holger Sieg, 1999. "Estimating Equilibrium Models of Local Jurisdictions," Journal of Political Economy, University of Chicago Press, vol. 107(4), pages 645-681, August.
  12. Amemiya, Takeshi, 1974. "The nonlinear two-stage least-squares estimator," Journal of Econometrics, Elsevier, vol. 2(2), pages 105-110, July.
  13. James N. Brown, 1983. "Structural Estimation in Implicit Markets," NBER Chapters, in: The Measurement of Labor Cost, pages 123-152 National Bureau of Economic Research, Inc.
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