Doctoral Dissertations


A modified approach for obtaining sieve bootstrap prediction intervals for time series


"The traditional Box-Jenkins approach to obtaining prediction intervals for stationary time seres assumes that the underlying distribution of the innovations is Gaussian. It is well known that deviations from this assumption can lead to prediction intervals with poor coverage. Nonparametric bootstrap-based procedures for obtaining prediction intervals overcome this handicap, but many early versions of such intervals for autoregressive moving average (ARMA) processes assume that the autoregressive and moving average orders, p, q respectively, are known, The sieve bootstrap, first introduced by Bühlmann in 1997, sidesteps this assumption for invertible time series by approximating the ARMA process by a finite autoregressive model whose order is estimated by using a model procedure such as the AICC. Existing sieve bootstrap methods in general, however, produces liberal prediction intervals due to several factors, including the use of residuals that underestimate the actual variance of the innovations and the failure of the methods to capture variations due to sampling error of some parameter estimates. In this dissertation, a modified sieve bootstrap approach, that corrects these deficiencies, is implemented to obtain prediction intervals for both univariate and multivariate time series. Monte Carlo simulations results show that the modifications provide prediction intervals that achieve nominal or near nominal coverage probabilities. Asymptotic results for the univariate series also establish the validity of the modified approach"--Abstract, page iii.


Samaranayake, V. A.

Committee Member(s)

Bryant, Richard
Drain, David
Wen, Xuerong
Gadbury, Gary L.


Mathematics and Statistics

Degree Name

Ph. D. in Mathematics


Missouri University of Science and Technology

Publication Date

Fall 2008


x, 150 pages

Note about bibliography

Includes bibliographical references (pages 145-149).


© 2008 Purna Mukhopadhyay, All rights reserved.

Document Type

Dissertation - Citation

File Type




Subject Headings

Autoregression (Statistics)
Bootstrap (Statistics)
Prediction (Logic)
Regression analysis

Thesis Number

T 9458

Print OCLC #


Link to Catalog Record

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