期刊名称：Discussion Papers in Economics / Department of Economics, University of York
出版年度：2015
卷号：2015
出版社：University of York
摘要：In order to achieve dimension reduction for the nonparametric functional coefficients and improve the estimation efficiency, in this paper we introduce a novel semiparametric estimation procedure which combines a principal component analysis of the functional coefficients and a Cholesky decomposition of the within-subject covariance matrices. Under some regularity conditions, we derive the asymptotic distribution for the proposed semiparametric estimators and show that the efficiency of the estimation of the (principal) functional coefficients can be improved when the within-subject covariance structure is correctly speci ed. Furthermore, we apply two approaches to consistently estimate the Cholesky decomposition, which avoid a possible misspeci cation of the within-subject covariance structure and ensure the efficiency improvement for the estimation of the (principal) functional coefficients. Some numerical studies including Monte Carlo experiments and an empirical application show that the developed semiparametric method works reasonably well in finite samples.
关键词：Cholesky decomposition; functional coefficients; local linear smoothing; principal component analysis; profile least squares; within-subject covariance
Loading...
