Obtaining Prediction Intervals for Farima Processes Using the Sieve Bootstrap

Editor(s)

Krutchkoff, Richard

Abstract

The sieve bootstrap (SB) prediction intervals for invertible autoregressive moving average (ARMA) processes are constructed using resamples of residuals obtained by fitting a finite degree autoregressive approximation to the time series. The advantage of this approach is that it does not require the knowledge of the orders, p and q, associated with the ARMA(p, q) model. Up until recently, the application of this method has been limited to ARMA processes whose autoregressive polynomials do not have fractional unit roots. The authors, in a 2012 publication, introduced a version of the SB suitable for fractionally integrated autoregressive moving average (FARIMA (p,d,q)) processes with 0

Department(s)

Mathematics and Statistics

Keywords and Phrases

forecasting; long memory processes; fractionally integrated time series; model-based bootstrap; ARFIMA processes

International Standard Serial Number (ISSN)

0094-9655

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2013 Taylor & Francis, All rights reserved.

Publication Date

01 Jan 2013

Link to Full Text

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