Bias-Corrected Bootstrap Prediction Intervals for Autoregressive Model: New Alternatives with Applications to Tourism Forecasting

摘要

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 bias-correction 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 small-sample 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.