ESTIMATION OF AUTOCOVARIANCE MATRICES FOR INFINITE DIMENSIONAL VECTOR LINEAR PROCESS

被引:9
作者
Bhattacharjee, Monika [1 ]
Bose, Arup [1 ]
机构
[1] Indian Stat Inst, Stat & Math Unit, Kolkata 700108, India
关键词
marginal variance-covariance matrix; parameter matrix; coefficient matrix; High-dimensional data; operator norm; banding; spatial variable; k-th order autocovariance matrix; variance-covariance matrix; IVAR; cross-sectional variables; consistency; convergence rate; DYNAMIC-FACTOR MODEL;
D O I
10.1111/jtsa.12063
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Consider an infinite dimensional vector linear process. Under suitable assumptions on the parameter space, we provide consistent estimators of the autocovariance matrices. In particular, under causality, this includes the infinite-dimensional vector autoregressive (IVAR) process. In that case, we obtain consistent estimators for the parameter matrices. An explicit expression for the estimators is obtained for IVAR(1), under a fairly realistic parameter space. We also show that under some mild restrictions, the consistent estimator of the marginal large dimensional variance-covariance matrix has the same convergence rate as that in case of i.i.d. samples.
引用
收藏
页码:262 / 281
页数:20
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