Local Multiplicative Bias Correction for Asymmetric Kernel Density Estimators

Matthias Hagmann () and Olivier Scaillet
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Matthias Hagmann: HEC-University of Lausanne and FAME

FAME Research Paper Series from International Center for Financial Asset Management and Engineering

Abstract: 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.

Keywords: semiparametric density estimation; asymmetric kernel; income distribution; loss distribution; health insurance; specification testing (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Date: 2003-09
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Local multiplicative bias correction for asymmetric kernel density estimators (2007)
Working Paper: Local Multiplicative Bias Correction For Asymmetric Kernel Density Estimators (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:fam:rpseri:rp91

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