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Local multiplicative bias correction for asymmetric kernel density estimators

  • Hagmann, M.
  • Scaillet, O.

We consider semiparametric asymmetric kernel density estimators when the unknown density has support on [0, ¥). We provide a unifying framework which 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 simple to implement. We provide applications to loss data from a large Swiss health insurer and Brazilian income data.

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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 141 (2007)
Issue (Month): 1 (November)
Pages: 213-249

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Handle: RePEc:eee:econom:v:141:y:2007:i:1:p:213-249
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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