The Annals of Statistics
- Ann. Statist.
- Volume 43, Number 3 (2015), 1117-1140.
Covariance matrix estimation and linear process bootstrap for multivariate time series of possibly increasing dimension
Multivariate time series present many challenges, especially when they are high dimensional. The paper’s focus is twofold. First, we address the subject of consistently estimating the autocovariance sequence; this is a sequence of matrices that we conveniently stack into one huge matrix. We are then able to show consistency of an estimator based on the so-called flat-top tapers; most importantly, the consistency holds true even when the time series dimension is allowed to increase with the sample size. Second, we revisit the linear process bootstrap (LPB) procedure proposed by McMurry and Politis [ J. Time Series Anal. 31 (2010) 471–482] for univariate time series. Based on the aforementioned stacked autocovariance matrix estimator, we are able to define a version of the LPB that is valid for multivariate time series. Under rather general assumptions, we show that our multivariate linear process bootstrap (MLPB) has asymptotic validity for the sample mean in two important cases: (a) when the time series dimension is fixed and (b) when it is allowed to increase with sample size. As an aside, in case (a) we show that the MLPB works also for spectral density estimators which is a novel result even in the univariate case. We conclude with a simulation study that demonstrates the superiority of the MLPB in some important cases.
Ann. Statist., Volume 43, Number 3 (2015), 1117-1140.
Received: January 2014
Revised: September 2014
First available in Project Euclid: 15 May 2015
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 62G09: Resampling methods
Secondary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]
Jentsch, Carsten; Politis, Dimitris N. Covariance matrix estimation and linear process bootstrap for multivariate time series of possibly increasing dimension. Ann. Statist. 43 (2015), no. 3, 1117--1140. doi:10.1214/14-AOS1301. https://projecteuclid.org/euclid.aos/1431695640
- Additional proofs, simulations and a real data example. In the supplementary material we provide proofs, additional supporting simulations and an application of the MLPB to German stock index data. The supplementary material to this paper is also available online at http://www.math.ucsd.edu/politis/PAPER/MLPBsupplement.pdf.