Scientific article
Open access

Local multiplicative bias correction for asymmetric kernel density estimators

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

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.

  • 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
Main files (1)
Article (Accepted version)
ISSN of the journal0304-4076

Technical informations

Creation01/14/2016 4:14:00 PM
First validation01/14/2016 4:14:00 PM
Update time03/15/2023 12:05:04 AM
Status update03/15/2023 12:05:03 AM
Last indexation01/16/2024 8:04:45 PM
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack