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Econometric Forecasting of Tourist Arrivals Using Bayesian Structural Time‐Series

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  • Antony Andrews
  • Sean Kimpton

Abstract

This article introduces the Bayesian structural time series (BSTS) as a potential tool for forecasting in the tourism literature. Using data on Australian tourist arrivals in New Zealand, the forecasting accuracy of the estimated model is evaluated using a fixed partitioning approach. The MAPE of the fitted model is 3.11 per cent for the validation stage and 2.75 per cent for the test stage. The BSTS outperforms two other competing models both in the validation and test stage. In addition to forecasting, BSTS also estimates the trend, trend slope, and seasonality that change over time.

Suggested Citation

  • Antony Andrews & Sean Kimpton, 2023. "Econometric Forecasting of Tourist Arrivals Using Bayesian Structural Time‐Series," Economic Papers, The Economic Society of Australia, vol. 42(2), pages 200-211, June.
    • Handle: RePEc:bla:econpa:v:42:y:2023:i:2:p:200-211
    DOI: 10.1111/1759-3441.12383
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    3. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, Enero-Abr.
    4. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
    5. Jonah Gabry & Daniel Simpson & Aki Vehtari & Michael Betancourt & Andrew Gelman, 2019. "Visualization in Bayesian workflow," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 389-402, February.
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