Local Whittle estimation of high-dimensional long-run variance and precision matrices
研究了高维时间序列中长期方差矩阵及其逆矩阵（精度矩阵）的估计方法，采用阈值和惩罚化的局部Whittle估计，在稀疏假设下允许序列数量随样本量增长，并提供了算法和模拟应用。
This work develops nonasymptotic theory for estimation of the long-run variance matrix and its inverse, the so-called precision matrix, for high-dimensional time series under general assumptions on the dependence structure including long-range dependence. The estimation involves shrinkage techniques, which are thresholding and penalizing versions of the classical multivariate local Whittle estimator. The results ensure consistent estimation in a double asymptotic regime where the number of component time series is allowed to grow with the sample size as long as the true model parameters are sparse. The key technical result is a concentration inequality of the local Whittle estimator for the long-run variance matrix around the true model parameters. In particular, it handles simultaneously the estimation of the memory parameters, which enter the underlying model. Novel algorithms for the considered procedures are proposed, and a simulation study and a data application are also provided.