The multiple hybrid bootstrap - Resampling multivariate linear processes

Authors:
Carsten Jentsch;Jens-Peter Kreiss
Affiliations:
-;-
Venue:
Journal of Multivariate Analysis
Year:
2010

Citing 3
Cited 1

Time series: theory and methods

Time series: theory and methods
Bootstrapping autoregressive and moving average parameter estimates of infinite order vector autoregressive processes

Journal of Multivariate Analysis
New Introduction to Multiple Time Series Analysis

New Introduction to Multiple Time Series Analysis

A frequency domain bootstrap for Whittle estimation under long-range dependence

Journal of Multivariate Analysis

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Abstract

The paper reconsiders the autoregressive aided periodogram bootstrap (AAPB) which has been suggested in Kreiss and Paparoditis (2003) [18]. Their idea was to combine a time domain parametric and a frequency domain nonparametric bootstrap to mimic not only a part but as much as possible the complete covariance structure of the underlying time series. We extend the AAPB in two directions. Our procedure explicitly leads to bootstrap observations in the time domain and it is applicable to multivariate linear processes, but agrees exactly with the AAPB in the univariate case, when applied to functionals of the periodogram. The asymptotic theory developed shows validity of the multiple hybrid bootstrap procedure for the sample mean, kernel spectral density estimates and, with less generality, for autocovariances.