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Forecasting Inbound Canadian Tourism: An Evaluation of Error Corrections Model Forecasts

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  • William Veloce

    (Department of Economics, Brock University, St Catharines, Ontario L2S 3A1, Canada)

Abstract

This paper computes and evaluates a variety of quantitative forecasts for inbound Canadian tourists, including the Error Corrections Model (ECM) and the traditional regression model forecasts. A number of forecasting methods are employed: naive to sophisticated, univariate to multivariate, time series and econometric. Forecasts for the number of inbound Canadian tourists are derived using data from four major markets: the USA, the UK, Germany and Japan. The evaluation of the forecasts is based on the Generalized Forecast Error Second Moment (GFESM) criterion developed by Clements and Hendry (1993) and the Adjusted Mean Absolute Percentage Error (AMAPE) criterion. The ECM forecasts performed best, while the traditional regression model forecasts performed poorly. In this study, using Canadian data, the development of an ECM (which entails careful analysis of the integration and co-integration properties of the variables) provides an improvement in forecast accuracy. Previous tourism studies have found less promising results concerning the performance of the ECM forecasts.

Suggested Citation

  • William Veloce, 2004. "Forecasting Inbound Canadian Tourism: An Evaluation of Error Corrections Model Forecasts," Tourism Economics, , vol. 10(3), pages 263-280, September.
    • Handle: RePEc:sae:toueco:v:10:y:2004:i:3:p:263-280
    DOI: 10.5367/0000000041895049
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    References listed on IDEAS

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    1. N. Kulendran, 1996. "Modelling Quarterly Tourist Flows to Australia Using Cointegration Analysis," Tourism Economics, , vol. 2(3), pages 203-222, September.
    2. Kulendran, N. & King, Maxwell L., 1997. "Forecasting international quarterly tourist flows using error-correction and time-series models," International Journal of Forecasting, Elsevier, vol. 13(3), pages 319-327, September.
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    Cited by:

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    2. Peng, Bo & Song, Haiyan & Crouch, Geoffrey I., 2014. "A meta-analysis of international tourism demand forecasting and implications for practice," Tourism Management, Elsevier, vol. 45(C), pages 181-193.
    3. Wai Kit Tsang & Dries F. Benoit, 2020. "Gaussian processes for daily demand prediction in tourism planning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 551-568, April.
    4. Lourenço, Nuno & Gouveia, Carlos Melo & Rua, António, 2021. "Forecasting tourism with targeted predictors in a data-rich environment," Economic Modelling, Elsevier, vol. 96(C), pages 445-454.
    5. Ogechi Adeola & Nathaniel Boso & Olaniyi Evans, 2018. "Drivers of international tourism demand in Africa," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 53(1), pages 25-36, January.
    6. Gunter, Ulrich & Önder, Irem, 2015. "Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data," Tourism Management, Elsevier, vol. 46(C), pages 123-135.
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    8. Gang Xie & Xin Li & Yatong Qian & Shouyang Wang, 2021. "Forecasting tourism demand with KPCA-based web search indexes," Tourism Economics, , vol. 27(4), pages 721-743, June.

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