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Scientific article
Open access
English

Local multiplicative bias correction for asymmetric kernel density estimators

Published inJournal of econometrics, vol. 141, no. 1, p. 213-249
Publication date2007
Abstract

We consider semiparametric asymmetric kernel density estimators when the unknown density has support on [0, ∞). We provide a unifying framework which relies on a local multiplicative bias correction, and contains asymmetric kernel versions of several semiparametric density estimators considered previously in the literature. This framework allows us to use popular parametric models in a nonparametric fashion and yields estimators which are robust to misspecification. We further develop a specification test to determine if a density belongs to a particular parametric family. The proposed estimators outperform rival non- and semiparametric estimators in finite samples and are easy to implement. We provide applications to loss data from a large Swiss health insurer and Brazilian income data.

Keywords
  • Semiparametric density estimation
  • Asymmetric kernel
  • Income distribution
  • Loss distribution
  • Health insurance
  • Specification testing
Citation (ISO format)
HAGMANN, M., SCAILLET, Olivier. Local multiplicative bias correction for asymmetric kernel density estimators. In: Journal of econometrics, 2007, vol. 141, n° 1, p. 213–249. doi: 10.1016/j.jeconom.2007.01.018
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Article (Accepted version)
accessLevelPublic
Identifiers
ISSN of the journal0304-4076
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Technical informations

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