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

Jae Kim, Haiyang Song, Kevin Wong, George Athanasopoulos () and Shen Liu

No 11/08, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: This paper evaluates the performance 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 Harvey's structural time series models. We use thirteen monthly time series for the number of tourist arrivals to Hong Kong and to Australia. The mean coverage rate and length of alternative prediction intervals are evaluated in an empirical setting. It is found that the prediction intervals from all models show satisfactory performance, except for those from the autoregressive model. In particular, those based on the bias-corrected bootstrap in general perform best, providing tight intervals with accurate coverage rates, especially when the forecast horizon is long.

Keywords: Automatic forecasting; Bootstrapping; Interval forecasting (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2008-12, Revised 2009-10
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-ore and nep-tur
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals (2011)
Journal Article: Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals (2011)
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