![Sample our Computer Science journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5928/TOC-Subject-Sample-2021-Computer-Science.jpg"})
Abstract
A procedure for estimating a time series model with structural change is proposed. Nonparametric bootstrap (block bootstrap or AR sieve) is applied to a series of estimates obtained through a modified forward search (FS) algorithm. The FS algorithm is implemented with overlapping and independent blocks of time points. The procedure can mitigate the difficulty in estimating when there is a temporary structural change. The simulation study indicated robustness of estimates from the estimation method when temporary structural change is introduced into the model provided that the time series is fairly long. As the effect of structural change persists in a longer period, the robustness of the bootstrap methods is further emphasized. We also provided a procedure for detecting the structural change and the subsequent adjustment of the overall model if indeed, there is a structural change.
