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.
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Publisher Info
Paper provided by Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland in its series DQE Working Papers with number
4.
Length: 15 pages 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. Handle: RePEc:fri:dqewps:wp0004
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