RT Journal Article
T1 Bootstrap predictive inference for ARIMA processes
A1 Pascual, Lorenzo
A1 Romo, Juan
A1 Ruiz Ortega, Esther
AB In this study, we propose a new bootstrap strategy to obtain prediction intervals for autoregressive integrated moving-average processes. Its main advantage over other bootstrap methods previously proposed for autoregressive integrated processes is that variability due to parameter estimation can be incorporated into prediction intervals without requiring the backward representation of the process. Consequently, the procedure is very flexible and can be extended to processes even if their backward representation is not available. Furthermore, its implementation is very simple. The asymptotic properties of the bootstrap prediction densities are obtained. Extensive finite-sample Monte Carlo experiments are carried out to compare the performance of the proposed strategy vs. alternative procedures. The behaviour of our proposal equals or outperforms the alternatives in most of the cases. Furthermore, our bootstrap strategy is also applied for the first time to obtain the prediction density of processes with moving-average components.
PB Wiley-Blackwell
SN 1467-9892(online)
SN 0143-9782(print)
YR 2004
FD 2004
LK https://hdl.handle.net/10016/4841
UL https://hdl.handle.net/10016/4841
LA eng
DS e-Archivo
RD 18 may. 2024