Covariance matrix estimation and linear process bootstrap for multivariate time series of possibly increasing dimension

DOI10.1214/14-AOS1301zbMath1320.62099arXiv1506.00816MaRDI QIDQ2352737

Dimitris N. Politis, Carsten Jentsch

Publication date: 6 July 2015

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1506.00816

zbMATH Keywords

bootstrapasymptoticscovariance matrixspectral densityhigh-dimensional datamultivariate time seriessample mean

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Inference from stochastic processes and spectral analysis (62M15) Nonparametric statistical resampling methods (62G09)

Related Items (11)

Model-free bootstrap for a general class of stationary time series ⋮ Simultaneous multiple change-point and factor analysis for high-dimensional time series ⋮ GLS estimation and confidence sets for the date of a single break in models with trends ⋮ Hypothesis testing for high-dimensional time series via self-normalization ⋮ Linear process bootstrap unit root test ⋮ Change-point detection in panel data via double CUSUM statistic ⋮ Estimation of autocovariance matrices for high dimensional linear processes ⋮ Discussion on: ``Bootstrap methods for dependent data: a review ⋮ Estimating wold matrices and vector moving average processes ⋮ Methods to compute prediction intervals: a review and new results ⋮ Covariance matrix estimation and linear process bootstrap for multivariate time series of possibly increasing dimension

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