Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals

Jae Kim, Kevin Wong, George Athanasopoulos, Shen Liu

Research output: Contribution to journalArticleResearchpeer-review

28 Citations (Scopus)


This paper evaluates the performances of prediction intervals generated from alternative time series models, in the context of tourism forecasting. The forecasting methods considered include the autoregressive (AR) model, the AR model using the bias-corrected bootstrap, seasonal ARIMA models, innovations state space models for exponential smoothing, and Harveya??s structural time series models. We use thirteen monthly time series for the number of tourist arrivals to Hong Kong and Australia. The mean coverage rates and widths of the alternative prediction intervals are evaluated in an empirical setting. It is found that all models produce satisfactory prediction intervals, except for the autoregressive model. In particular, those based on the bias-corrected bootstrap perform best in general, providing tight intervals with accurate coverage rates, especially when the forecast horizon is long.
Original languageEnglish
Pages (from-to)887 - 901
Number of pages15
JournalInternational Journal of Forecasting
Issue number3
Publication statusPublished - 2011

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