Chiang Mai Journal of Science

Simple Bootstrap Predictor Based on Unit Root Test for Autoregressive Processes

Paper Type	Contributed Paper
Title	Simple Bootstrap Predictor Based on Unit Root Test for Autoregressive Processes
Author	Wararit Panichkitkosolkul and Kamon Budsaba
Email	wararit@mathstat.sci.tu.ac.th
Abstract: The Gaussian-based predictors for time series work reasonably well when the underlying distributional assumption holds. An alternative method is the bootstrap approach which does not assume a Gaussian error distribution. Recent work of Cai and Davies [1] presented a simple and model-free bootstrap method for time series. Furthermore, there is significant simulation evidence that preliminary unit root tests can be used to improve the efficiency of a predictor and prediction interval. In this paper, we develop a new multi-step-ahead simple bootstrap predictor based on unit root testing by using the simple bootstrap method for time series. The estimated absolute bias and prediction mean square error of the multi-step-ahead simple bootstrap predictor and multi-step-ahead simple bootstrap predictor based on unit root test are compared via Monte Carlo simulation studies. Simulation results show that the unit root test improves the accuracy of the multi-step-ahead simple bootstrap predictor for autoregressive processes for near-non-stationary and non-stationary processes. The performance of these simple bootstrap predictors is illustrated through an empirical application to a set of monthly closings of the Dow-Jones industrial index.
Start & End Page	625 - 633
Received Date	2015-07-08
Revised Date
Accepted Date	2016-10-10
Full Text	Download
Keyword	prediction, bootstrap approach, simulation study, time series
Volume	Vol.45 No.1 (January 2018)
DOI
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