Local Multiplicative Bias Correction for Asymmetric Kernel Density Estimators
62 Pages Posted: 30 Jun 2004
Date Written: September 2003
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
JEL Classification: C13, C14
Suggested Citation: Suggested Citation
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