Asymptotic Properties of Sieve Bootstrap Prediction Intervals For Farima Processes

Author

Maduka Rupasinghe
V. A. Samaranayake, Missouri University of Science and TechnologyFollow

Editor(s)

Koul, H. L. and Xiao, Y.

Abstract

The sieve bootstrap is a resampling technique that uses autoregressive approximations of order p to model invertible linear time series, where p is allowed to go to infinity with sample size n. The asymptotic properties of sieve bootstrap prediction intervals for stationary invertible linear processes with short memory have been established in the past. In this paper, we extend these results to long memory (FARIMA) processes. We show that under certain regularity conditions the sieve bootstrap provides consistent estimators of the conditional distribution of future values of FARIMA processes, given the observed data.

Recommended Citation

M. Rupasinghe and V. A. Samaranayake, "Asymptotic Properties of Sieve Bootstrap Prediction Intervals For Farima Processes," Statistical and Probability Letters, Elsevier, Jan 2012.

The definitive version is available at https://doi.org/10.1016/j.spl.2012.07.011

Department(s)

Mathematics and Statistics

Keywords and Phrases

Forecast Intervals; Fractionally Integrated Time Series; Long Memory Processes; Autoregressive Approximations

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2012 Elsevier, All rights reserved.

Publication Date

01 Jan 2012