PREDICTION INTERVALS AND REGIONS FOR MULTIVARIATE TIME
SERIES MODELS WITH SIEVE BOOTSTRAP
Roman Różański
Grzegorz Chłapiński
Marcin Hławka
Krzysztof Jamróz
Maciej Kawecki
Adam Zagdański
Abstract: In the paper, the construction of unconditional bootstrap prediction intervals and
regions for some class of second order stationary multivariate linear time series models is
considered. Our approach uses the sieve bootstrap procedure introduced by Kreiss (1992) and
Bühlmann (1997). Basic theoretical results concerning consistency of the bootstrap
replications and the bootstrap prediction regions are proved. We present a simulation
study comparing the proposed bootstrap methods with the Box–Jenkins approach.
2010 AMS Mathematics Subject Classification: Primary: 62M10, 62M20; Secondary:
62G09, 62G15, 62G05.
Keywords and phrases: Multivariate time series models, vector of time series, sieve
bootstrap, prediction regions, simultaneous prediction intervals.