Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/72201
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Title: Bagging in tourism demand modeling and forecasting
Authors: Athanasopoulos, G
Song, H 
Sun, JA
Issue Date: 1-Jan-2018
Source: Journal of travel research, 1 Jan. 2018, p. 52-68
Abstract: This study introduces bootstrap aggregation (bagging) in modeling and forecasting tourism demand. The aim is to improve the forecast accuracy of predictive regressions while considering fully automated variable selection processes which are particularly useful in industry applications. The procedures considered for variable selection is the general-to-specific (GETS) approach based on statistical inference and stepwise search procedures based on a measure of predictive accuracy (MPA). The evidence based on tourist arrivals from six source markets to Australia overwhelmingly suggests that bagging is effective for improving the forecasting accuracy of the models considered.
Keywords: Australia
Bootstrap aggregation
Model selection
Predictive regression
Publisher: SAGE Publications
Journal: Journal of travel research 
ISSN: 0047-2875
EISSN: 1552-6763
DOI: 10.1177/0047287516682871
Rights: This is the accepted version of the publication Athanasopoulos, G., Song, H., & Sun, J. A., Bagging in tourism demand modeling and forecasting, Journal of Travel Research (Volume 57 and issue 1) pp. 52-68. Copyright © 2017 (The Author(s)). DOI: 10.1177/0047287516682871.
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