Doctoral Dissertations


Bootstrap prediction intervals for multivariate time series

Keywords and Phrases

Prediction intervals


"The theory and methodology of obtaining bootstrap prediction intervals for univariate time series using the forward representation of the series is extended to vector autoregressive (VAR) models. Kim has shown that simultaneous prediction intervals based on the Bonferroni method and the backward representation of the time series achieve coverage close to nominal when the parameter estimates are corrected for small sample bias. To utilize his method, it is necessary to assume that the innovations are normally distributed to maintain independence of the innovations associated with the backward representation of the time series. This assumption is not necessary if the forward representation is used. Bootstrap prediction intervals based on the forward representation of the time series, are less restrictive and thus can also be adapted for time series that do not have a backward representation. The asymptotic validity of the proposed bootstrap method is established and small sample properties are studied using Monte Carlo simulation"--Abstract, leaf iii.


Mathematics and Statistics

Degree Name

Ph. D. in Mathematics


University of Missouri--Rolla

Publication Date

Summer 2005


x, 170 leaves

Note about bibliography

Includes bibliographical references (leaves 168-169).


© 2005 Florian Sebastian Rueck, All rights reserved.

Document Type

Dissertation - Citation

File Type




Library of Congress Subject Headings

Time-series analysis
Bootstrap (Statistics)
Prediction (Logic)
Multivariate analysis

Thesis Number

T 8822

Print OCLC #


Link to Catalog Record

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