Nonparametric estimation of large covariance matrices with conditional sparsity
Nonparametric estimation of large covariance matrices with conditional sparsity
复制标题
条件稀疏性大协方差矩阵的非参数估计
DOI：
10.1016/j.jeconom.2020.09.002
复制
发表时间：
2021-04-30
影响因子：
6.3
通讯作者：
Leng, Chenlei
中科院分区：
文献类型：
--
作者：
Wang, Hanchao;Peng, Bin;Leng, Chenlei
研究了条件稀疏结构下协方差矩阵的估计问题。我们克服了使用因子结构估计密集矩阵的挑战，通过假设随机噪声协方差的稀疏性来估计大维矩阵的挑战，以及通过允许因子负载平滑变化来估计变化矩阵的挑战。提出了一种结合广义收缩的核加权估计方法。在一定的技术条件下，我们得到了该估计方法的一致相合性，并得到了收敛速率。数值研究包括模拟和经验应用，以检查开发的方法的有限样本性能。(C) 2020 Elsevier B.V.版权所有
This paper studies estimation of covariance matrices with conditional sparse structure. We overcome the challenge of estimating dense matrices using a factor structure, the challenge of estimating large-dimensional matrices by postulating sparsity on covariance of random noises, and the challenge of estimating varying matrices by allowing factor loadings to smoothly change. A kernel-weighted estimation approach combined with generalised shrinkage is proposed. Under some technical conditions, we derive uniform consistency for the developed estimation method and obtain convergence rates. Numerical studies including simulation and an empirical application are presented to examine the finite-sample performance of the developed methodology. (C) 2020 Elsevier B.V. All rights reserved.