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Local Multiplicative Bias Correction for Asymmetric Kernel Density Estimators

  • Matthias HAGMANN


    (HEC-University of Lausanne and FAME)

  • Olivier SCAILLET


    (HEC-University of Geneva and FAME)

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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Paper provided by International Center for Financial Asset Management and Engineering in its series FAME Research Paper Series with number rp91.

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Date of creation: Sep 2003
Date of revision:
Handle: RePEc:fam:rpseri:rp91
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  1. Olivier RENAULT & Olivier SCAILLET, 2003. "On the Way to Recovery: A Nonparametric Bias Free Estimation of Recovery Rate Densities," FAME Research Paper Series rp83, International Center for Financial Asset Management and Engineering.
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  16. Cox, John C & Ingersoll, Jonathan E, Jr & Ross, Stephen A, 1985. "A Theory of the Term Structure of Interest Rates," Econometrica, Econometric Society, vol. 53(2), pages 385-407, March.
  17. Frank A Cowell & Francisco H.G. Ferreira & Julie Litchfield, 1996. "Income Distribution in Brazil 1981-1990: Parametric and Non-Parametric Approaches," STICERD - Distributional Analysis Research Programme Papers 21, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  18. Gozalo, Pedro & Linton, Oliver, 2000. "Local nonlinear least squares: Using parametric information in nonparametric regression," Journal of Econometrics, Elsevier, vol. 99(1), pages 63-106, November.
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