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Hedonic Prices and Colinearity: An Empirical Comparison of Statistical and Neuronal Solutions

Author

Listed:
  • Pierre Desmet

    (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique, ESSEC Business School)

Abstract

The hedonic price method is well adapted to the calculation of relative prices and the estimation of the quality price relationship for a complex product. The main weakness lies in the use of multiple regression for the evaluation of the coefficients when there is very little data and when the variables are correlated. In this article, various methods, statistical and neuronal, are compared from both the predictive capacity point of view as well as that of the facial validity of the expected results. The neuronal approach is globally more successful than PLS regression but neither of the two methods leads to an acceptable solution to the problems of interpretation of the coefficients (signs and values ) stemming from colinearity.

Suggested Citation

  • Pierre Desmet, 2000. "Hedonic Prices and Colinearity: An Empirical Comparison of Statistical and Neuronal Solutions," Post-Print halshs-00143422, HAL.
    • Handle: RePEc:hal:journl:halshs-00143422
    as

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    Cited by:

    1. Manuel Landajo & Celia Bilbao & Amelia Bilbao, 2012. "Nonparametric neural network modeling of hedonic prices in the housing market," Empirical Economics, Springer, vol. 42(3), pages 987-1009, June.

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