Beer, Michael 2007-01-20 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. application/pdf https://folia.unifr.ch/global/documents/302714 https://folia.unifr.ch/documents/302714/files/WP_DQE_04.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.1007/s10182-006-0015-9 info:eu-repo/semantics/openAccess License undefined AStA Advances in Statistical Analysis. - 2007, vol. 91, no. 1, p. 77-92 hedonic regression hedonic price indices bootstrap methods wild bootstrap confidence intervals used cars info:eu-repo/classification/udc/33 Bootstrapping a Hedonic Price Index: Experience from Used Cars Data http://purl.org/coar/resource_type/c_6501