Obtaining Prediction Intervals for Farima Processes Using the Sieve Bootstrap
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
M. Rupasinghe et al., "Obtaining Prediction Intervals for Farima Processes Using the Sieve Bootstrap," Journal of Statistical Computation and Simulation, Taylor & Francis, Jan 2013.
The definitive version is available at https://doi.org/10.1080/00949655.2013.781271
Mathematics and Statistics
Keywords and Phrases
forecasting; long memory processes; fractionally integrated time series; model-based bootstrap; ARFIMA processes
Article - Journal
© 2013 Taylor & Francis, All rights reserved.