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

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  • Jae H. Kim

    ()

  • Haiyang Song
  • Kevin Wong
  • George Athanasopoulos

    ()

  • Shen Liu

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.

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File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2008/wp11-08.pdf
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Bibliographic Info

Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 11/08.

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Length: 29 pages
Date of creation: Dec 2008
Date of revision: Oct 2009
Handle: RePEc:msh:ebswps:2008-11

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Keywords: Automatic forecasting; Bootstrapping; Interval forecasting;

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Cited by:
  1. Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C., 2011. "The tourism forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 822-844.
  2. Liu, Shen & Maharaj, Elizabeth Ann & Inder, Brett, 2014. "Polarization of forecast densities: A new approach to time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 345-361.

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