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Seasonal Adjustment versus Seasonality Modelling: Effect on Tourism Demand Forecasting

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  • Amira Gasmi

Abstract

In this study, we treat the seasonal variation in monthly time series in the context of the Western-European tourism demand for Tunisia, by presenting different techniques of detection of seasonality and the parametric and non-parametric approaches of seasonal adjustment. Then, we compare the forecasting performance of these methods. The empirical results militate in favour of the TRAMO-SEATS method. In fact, this approach provides the best forecast. In terms of forecasting efficiency, we note in addition, that the modelling of the seasonal variation using seasonal ARIMA model (SARIMA) may lead to better predictive results compared with other techniques of seasonal adjustment used in this research, namely: the X-12-ARIMA, regression on seasonal dummies and the ratio-to-moving average methods.

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  • Amira Gasmi, 2013. "Seasonal Adjustment versus Seasonality Modelling: Effect on Tourism Demand Forecasting," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 3(4), pages 1-11.
    • Handle: RePEc:spt:admaec:v:3:y:2013:i:4:f:3_4_11
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    Cited by:

    1. H. M. Ertugrul & S. Yildirim & F. Ayhan, 2017. "An Investigation of Stationarity Properties of the Turkish Tourism Income Variable," International Econometric Review (IER), Econometric Research Association, vol. 9(2), pages 37-49, September.

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