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Identification and Estimation of Hedonic Models

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  • Ivar Ekeland
  • James J. Heckman
  • Lars P. 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 empirical 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.

Suggested Citation

  • Ivar Ekeland & James J. Heckman & Lars P. Nesheim, 2003. "Identification and Estimation of Hedonic Models," NBER Working Papers 9910, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:9910
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    References listed on IDEAS

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    1. 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 60-109, February.
    2. 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.
    3. 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 157-166, February.
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    6. Amemiya, Takeshi, 1974. "The nonlinear two-stage least-squares estimator," Journal of Econometrics, Elsevier, vol. 2(2), pages 105-110, July.
    7. 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.
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    9. James J. Heckman & Edward J. Vytlacil, 2000. "Local Instrumental Variables," NBER Technical Working Papers 0252, National Bureau of Economic Research, Inc.
    10. 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.
    11. 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.
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    14. Lars Nesheim, 2004. "Equilibrium Sorting of Heterogeneous Consumers Across Locations," Econometric Society 2004 North American Summer Meetings 337, Econometric Society.
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    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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