期刊名称：CORE Discussion Papers / Center for Operations Research and Econometrics (UCL), Louvain
出版年度：2007
卷号：1
出版社：Center for Operations Research and Econometrics (UCL), Louvain
摘要：In this paper we estimate density functions for positive multivariate data. We propose a
semiparametric approach. The estimator combines gamma kernels or local linear kernels,
also called boundary kernels, for the estimation of the marginal densities with
semiparametric copulas to model the dependence. This semiparametric approach is robust
both to the well known boundary bias problem and the curse of dimensionality problem. We
derive the mean integrated squared error properties, including the rate of convergence, the
uniform strong consistency and the asymptotic normality. A simulation study investigates
the finite sample performance of the estimator. We find that univariate least squares cross
validation, to choose the bandwidth for the estimation of the marginal densities, works well
and that the estimator we propose performs very well also for data with unbounded support.
Applications in the field of finance are provided.
关键词：asymptotic properties, asymmetric kernels, boundary bias, copula, curse of
dimension, least squares cross validation.
