Summary
We consider using bootstrap method for stationary time series problems concerned with prediction intervals for future observations and confidence intervals for the spectral density. Our approach relies on the sieve bootstrap procedure introduced by Bühlmann (1996, 1997) for stationary AR(∞) processes.
We extend the method of obtaining prediction intervals which has been proposed by Stine (1987) for autoregressive time series of known order and compare it with more traditional Gaussian strategy. The introduced sieve-bootstrap approach for constructing confidence intervals for the spectrum is also compared with X 2 approximation method and other bootstrap procedure proposed by Pranke and Härdie (1992).
The accuracy of the presented methods is verified via numerical comparison including both Gaussian and non-Gaussian data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brockwell, P. and Davies, R. (1987). Time Series Theory and Method. Springer-Verlag.
Bühlmann, P. (1996). Confidence regions for trends in time series: a simultaneous approach with a sieve bootstrap. Technical report, University of California Berkeley.
Bühlmann, P. (1997). Sieve bootstrap for time series. Bernouli, 3(2):123–148.
Franke, J. and Härdie, W. (1992) On bootstraping kernel spectral estimates. The Annals of Statistics, 20(1):121–145.
Grenander, U. (1981). Abstract Inference. Wiley, New York.
Stine, R. A. (1987). Estimating properties of autoregressive forecast. Journal of the American Statistical Association, 82(400):1073–1078.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zagdański, A. (2000). Using the Sieve Bootstrap Method in Time Series Analysis. In: Inderfurth, K., Schwödiauer, G., Domschke, W., Juhnke, F., Kleinschmidt, P., Wäscher, G. (eds) Operations Research Proceedings 1999. Operations Research Proceedings 1999, vol 1999. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-58300-1_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-58300-1_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67094-0
Online ISBN: 978-3-642-58300-1
eBook Packages: Springer Book Archive