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An Assessment of Combining Tourism Demand Forecasts over Different Time Horizons

Shen, S, Li, G and Song, H (2008) An Assessment of Combining Tourism Demand Forecasts over Different Time Horizons Journal of Travel Research, 47 (2). pp. 197-207.


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This study investigates the performance of combination forecasts in comparison to individual forecasts. The empirical study focuses on the UK outbound leisure tourism demand for the USA. The combination forecasts are based on the competing forecasts generated from seven individual forecasting techniques. The three combination methods examined in this study are: the simple average combination method, the variance-covariance combination method and the discounted mean square forecast error method. The empirical results suggest that combination forecasts overall play an important role in the improvement of forecasting accuracy in that they are superior to the best of the individual forecasts over different forecasting horizons. The variance-covariance combination method turns out to be the best among the three combination methods. Another finding of this study is that the encompassing test does not contribute significantly to the improved accuracy of combination forecasts. This study provides robust evidence of the efficiency of combination forecasts.

Item Type: Article
Divisions : Faculty of Arts and Social Sciences > School of Hospitality and Tourism Management
Authors :
Shen, S
Song, H
Date : November 2008
DOI : 10.1177/0047287508321199
Uncontrolled Keywords : combination forecast, tourism demand, econometric model, forecast performance, encompassing test, ECONOMIC FORECASTS, COMBINATION, COINTEGRATION, MODELS, TESTS, REGRESSION, ACCURACY, SERIES
Additional Information : This is an author-prepared version. The final, definitive version of this paper has been published in Journal of Travel Research, Vol. 47, No. 2, 197-207 (2008) by SAGE Publications Ltd. © 2008 Sage. All rights reserved.
Depositing User : Mr Adam Field
Date Deposited : 27 May 2010 14:37
Last Modified : 16 Jan 2019 16:21

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