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Comparing Interval Restricted Estimators in Hedonic Pricing / Ein Vergleich intervallrestringierter Schätzverfahren in der hedonischen Preismessung

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  • Knautz Henning

    (Institut für Statistik und Ökonometrie, Universität Hamburg, Von-Melle- Park 5, D-20146 Hamburg)

Abstract

In hedonic pricing models there is often prior knowledge available which has the form of interval constraints on the unknown coefficients. These are stemming for example from considerations of submarkets for the characteristics involved. In this article we briefly discuss some well known estimators that allow for incorporation of this knowledge. Additionally we introduce two new promising approaches for the same purpose: a modified Bayesian approach and a method applying fuzzy interval constraints. Using data on housing prices we present the results of a Monte Carlo experiment in which these estimators are compared. It turns out that constrained estimation is promising especially in the situation of high multicollinearity and moderate R2 which is typical for hedonic pricing models. We illustrate that estimates and confidence intervals for the unknown coefficients can be improved substantially compared with the conventional unrestricted estimation.

Suggested Citation

  • Knautz Henning, 2000. "Comparing Interval Restricted Estimators in Hedonic Pricing / Ein Vergleich intervallrestringierter Schätzverfahren in der hedonischen Preismessung," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 220(5), pages 552-564, October.
  • Handle: RePEc:jns:jbstat:v:220:y:2000:i:5:p:552-564
    DOI: 10.1515/jbnst-2000-0505
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    References listed on IDEAS

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    2. Hoffmann, Johannes, 1998. "Probleme der Inflationsmessung in Deutschland," Discussion Paper Series 1: Economic Studies 1998,01, Deutsche Bundesbank.
    3. Gilley, Otis W & Pace, R Kelley, 1995. "Improving Hedonic Estimation with an Inequality Restricted Estimator," The Review of Economics and Statistics, MIT Press, vol. 77(4), pages 609-621, November.
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