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Efficient estimation of parameters in marginals in semiparametric multivariate models

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  • Valentyn Panchenko

    (University of New South Wales)

  • Artem Prokhorov

    (Concordia University and CIREQ)

Abstract

Recent literature on semiparametric copula models focused on the situation when the marginals are specified nonparametrically and the copula function is given a parametric form. For example, this setup is used in Chen, Fan and Tsyrennikov (2006) [Efficient Estimation of Semiparametric Multivariate Copula Models, JASA] who focus on efficient estimation of copula parameters. We consider a reverse situation when the marginals are specified parametrically and the copula function is modelled nonparametrically. This setting is no less relevant in applications. We use the method of sieve for efficient estimation of parameters in marginals, derive its asymptotic distribution and show that the estimator is semiparametrically efficient. Simulations suggest that the sieve MLE can be up to 40% more efficient relative to QMLE depending on the strength of dependence between the marginals. An application using insurance company loss and expense data demonstrates empirical relevance of this setting.

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File URL: http://alcor.concordia.ca/~aprokhor/papers/sieve.pdf
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Bibliographic Info

Paper provided by Concordia University, Department of Economics in its series Working Papers with number 11001.

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Date of creation: Jan 2011
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Handle: RePEc:crd:wpaper:11001

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  1. Chen, Xiaohong & Fan, Yanqin, 2006. "Estimation of copula-based semiparametric time series models," Journal of Econometrics, Elsevier, vol. 130(2), pages 307-335, February.
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