Local multiplicative bias correction for asymmetric kernel density estimators |
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Authors: | M. Hagmann O. Scaillet |
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Affiliation: | 1. Concordia Advisors and Swiss Finance Institute, Unit 112 Harbour Yard, London SW10 OXD, London, UK;2. HEC Genève and Swiss Finance Institute, UNIMAIL, 102 Bd Carl Vogt, CH-1211 Genève, Switzerland |
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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. |
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Keywords: | C13 C14 |
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