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Forecasting long-haul tourism demand for Hong Kong using error correction models

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  • Koon Nam Lee
Abstract
Forecasting accuracy is particularly important when forecasting tourism demand on account of the perishable nature of the product. This study compares a range of forecasting models in the context of predicting annual tourist flows into Hong Kong from the major long-haul markets of the US, the UK, Germany and major short-haul markets of China, Japan and Taiwan. Econometric forecasting models considered included Error Correction Models (ECMs) based on Permanent Income-Life Cycle (PI-LC) hypothesis (PI-LC ECM) and alternative cointegration approaches: Engle and Granger (1987), Johansen (1988), and Ordinary Least Square (OLS) approaches. Both Autoregressive Integrated Moving Average (ARIMA) and no change model (hereafter NAIVE) models are used as a benchmark time series model for accuracy comparisons. It was hypothesized that PI-LC ECM is a better forecasting model particularly for long-haul tourism demand. The objective of this article is to investigate whether the application of PI-LC ECM could improve the forecasting performance of econometric models relative to time series models. The forecasting results indicate that the PI-LC ECM based on the Engle-Granger (1987) approach produces more accurate forecasts than other alternative forecasting models for all long-haul markets based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) criteria. Overall, PI-LC ECMs produce better forecasts of tourism demand than the OLS, ARIMA and NAIVE models for all origin markets and all time horizons.

Suggested Citation

  • Koon Nam Lee, 2011. "Forecasting long-haul tourism demand for Hong Kong using error correction models," Applied Economics, Taylor & Francis Journals, vol. 43(5), pages 527-549.
    • Handle: RePEc:taf:applec:v:43:y:2011:i:5:p:527-549
    DOI: 10.1080/00036840802599743
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    Cited by:

    1. Tsui, Wai Hong Kan & Fu, Xiaowen & Yin, Chuanzhong & Zhang, Huaxin, 2021. "Hong Kong's aviation and tourism growth - An empirical investigation," Journal of Air Transport Management, Elsevier, vol. 93(C).
    2. Dewansh Raheja & R. Guo & S. M. Phyoe & Y. X. Lee & Z. W. Zhong, 2017. "Air Traffic and Economic Output: Projections for ASEAN," International Journal of Business and Administrative Studies, Professor Dr. Bahaudin G. Mujtaba, vol. 3(3), pages 92-99.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," IREA Working Papers 201417, University of Barcelona, Research Institute of Applied Economics, revised May 2014.
    4. Musallam Abedtalas, 2015. "The Determinants of Tourism Demand in Turkey," Journal of Economics and Behavioral Studies, AMH International, vol. 7(4), pages 90-105.
    5. Jiao, Xiaoying & Li, Gang & Chen, Jason Li, 2020. "Forecasting international tourism demand: a local spatiotemporal model," Annals of Tourism Research, Elsevier, vol. 83(C).
    6. Eden Xiaoying Jiao & Jason Li Chen, 2019. "Tourism forecasting: A review of methodological developments over the last decade," Tourism Economics, , vol. 25(3), pages 469-492, May.
    7. Hu, Yi & Xiao, Jin & Deng, Ying & Xiao, Yi & Wang, Shouyang, 2015. "Domestic air passenger traffic and economic growth in China: Evidence from heterogeneous panel models," Journal of Air Transport Management, Elsevier, vol. 42(C), pages 95-100.
    8. Armand Viljoen & Andrea Saayman & Melville Saayman, 2019. "Determinants influencing inbound arrivals to Africa," Tourism Economics, , vol. 25(6), pages 856-883, September.
    9. Zdravko Šergo, 2020. "The wealth effect and tourism – ARDL modeling and Granger causality in selected EU countries," Tourism and Hospitality Management, University of Rijeka, Faculty of Tourism and Hospitality Management, vol. 26(1), pages 195-212, June.
    10. Brian K. Masinde & Steven Buigut & Joseph K. Mung¡¯atu, 2016. "Modelling the Temporal Effect of Terrorism on Tourism in Kenya," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(12), pages 10-20, December.

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