Bootstrapping a Hedonic Price Index: Experience from Used Cars Data
Every hedonic price index is an estimate of an unknown economic parameter. It depends, in practice, on one or more random samples of prices and characteristics of a certain good. Bootstrap resampling methods provide a tool for quantifying sampling errors. Following some general reflections on hedonic elementary price indices, this paper proposes a case-based, a model-based, and a wild bootstrap approach for estimating confidence intervals for hedonic price indices. Empirical results are obtained for a data set on used cars in Switzerland. A simple and an enhanced adaptive semi-logarithmic model are fit to monthly samples, and bootstrap confidence intervals are estimated for Jevons-type hedonic elementary price indices.
|Date of creation:||22 Jul 2005|
|Date of revision:||20 Jan 2007|
|Publication status:||Published in AStA Advances in Statistical Analysis, 2007, vol. 91, no. 1, pp. 77-92.|
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- Bruce Curry & Peter Morgan & Mick Silver, 2001. "Hedonic regressions: mis-specification and neural networks," Applied Economics, Taylor & Francis Journals, vol. 33(5), pages 659-671.
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- Brachinger, Hans Wolfgang, 2002. "Statistical Theory of Hedonic Price Indices," DQE Working Papers 1, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland, revised Aug 2003.
- James G. MacKinnon, 2002. "Bootstrap inference in econometrics," Canadian Journal of Economics, Canadian Economics Association, vol. 35(4), pages 615-645, November.
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