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Title

Local multiplicative bias correction for asymmetric kernel density estimators

Authors
Hagmann, M.
Published in Journal of Econometrics. 2007, vol. 141, no. 1, p. 213-249
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 estimationAsymmetric kernelIncome distributionLoss distributionHealth insuranceSpecification testing
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Research group Geneva Finance Research Institute (GFRI)
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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. https://archive-ouverte.unige.ch/unige:79881

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Deposited on : 2016-01-22

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