RT Generic
T1 Bootstrap prediction intervals for power-transformed time series
A1 Pascual, Lorenzo
A1 Romo, Juan
A1 Ruiz Ortega, Esther
AB In this paper we propose a bootstrap resampling scheme to construct prediction intervals for future values of a variable after a linear ARIMA model has been fitted to a power transformation of it. The advantages over existing methods for computing prediction intervals of power transformed time series are that the proposed bootstrap intervals incorporate the variability due to parameter estimation, and do not rely on distributional assumptions neither on the original variable nor on the transformed one. We show the good behavior of the bootstrap approach versus alternative procedures by means of Monte Carlo experiments. Finally, the procedure is illustrated by analysing three real time series data sets.
YR 2001
FD 2001-01
LK https://hdl.handle.net/10016/150
UL https://hdl.handle.net/10016/150
LA eng
LA eng
DS e-Archivo
RD 1 sept. 2024