Effect of Seasonality Treatment on the Forecasting Performance of Tourism Demand Models

Shujie Shen, Gang Li and Haiyan Song
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Shujie Shen: Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK
Gang Li: School of Management, University of Surrey, Guildford GU2 7XH, UK
Haiyan Song: School of Hotel and Tourism Management, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China

Tourism Economics, 2009, vol. 15, issue 4, 693-708

Abstract: This study provides a comprehensive comparison of the performance of the commonly used econometric and time-series models in forecasting seasonal tourism demand. The empirical study is carried out based on the demand for outbound leisure tourism by UK residents to seven destination countries: Australia, Canada, France, Greece, Italy, Spain and the USA. In the modelling exercise, the seasonality of the data is treated using the deterministic seasonal dummies, seasonal unit root test techniques and the unobservable component method. The empirical results suggest that no single forecasting technique is superior to the others in all situations. As far as overall forecast accuracy is concerned, the Johansen maximum likelihood error correction model outperforms the other models. The time-series models also show superior performance in dealing with seasonality. However, the time-varying parameter model performs relatively poorly in forecasting seasonal tourism demand. This empirical evidence suggests that the methods of seasonality treatment affect the forecasting performance of the models and that the pre-test for seasonal unit roots is necessary and can improve forecast accuracy.

Keywords: seasonality; tourism demand; forecasting; seasonal unit roots; econometric model; time-series model (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:toueco:v:15:y:2009:i:4:p:693-708

DOI: 10.5367/000000009789955116

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