Bias-corrected bootstrap prediction intervals for autoregressive model: New alternatives with applications to tourism forecasting

Jae H. Kim, Haiyan Song, Kevin K F Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

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.
Original languageEnglish
JournalJournal of Forecasting
Volume29
Issue number7
DOIs
Publication statusPublished - 1 Jan 2010

Keywords

  • Bias-correction
  • Stationarity-correction
  • Time series
  • Tourist arrivals

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications
  • Strategy and Management
  • Management Science and Operations Research
  • Statistics, Probability and Uncertainty

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