Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22330
Title: Bias-corrected bootstrap prediction intervals for autoregressive model : new alternatives with applications to tourism forecasting
Authors: Kim, JH
Song, H 
Wong, KKF
Keywords: Bias-correction
Stationarity-correction
Time series
Tourist arrivals
Issue Date: 2010
Publisher: John Wiley and Sons
Source: Journal of forecasting, 2010, v. 29, no. 7 How to cite?
Journal: Journal of Forecasting 
Abstract: This paper proposes the use of the bias-corrected bootstrap for interval forecasting of an autoregressive time series with an arbitrary number of deterministic components. We use the bias-corrected bootstrap based on two alternative biascorrection methods: the bootstrap and an analytic formula based on asymptotic expansion. We also propose a new stationarity-correction method, based on stable spectral factorization, as an alternative to Kilian's method exclusively used in past studies. A Monte Carlo experiment is conducted to compare smallsample properties of prediction intervals. The results show that the bias-corrected bootstrap prediction intervals proposed in this paper exhibit desirable small-sample properties. It is also found that the bootstrap bias-corrected prediction intervals based on stable spectral factorization are tighter and more stable than those based on Kilian's stationarity-correction. The proposed methods are applied to interval forecasting for the number of tourist arrivals in Hong Kong.
URI: http://hdl.handle.net/10397/22330
ISSN: 0277-6693
DOI: 10.1002/for.1150
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