Does the combination of models with different explanatory variables improve tourism demand forecasting performance?
Authors: Wu, X. and Blake, A.
Journal: Tourism Economics
Volume: 29
Issue: 8
Pages: 2032-2056
ISSN: 1354-8166
DOI: 10.1177/13548166221132645
Abstract:The aim of this study is to assess whether combining econometric models with different explanatory variables can contribute to better tourism demand forecasts. Inbound tourism demand to the UK from seven leading markets is forecast, respectively, based on quarterly data using both individual and combination models. Causal econometric models that serve as constituents in combination take two specifications which are different in identified influencing factors. The empirical results show that generally including different explanatory variables in combination can produce better predictions according to both predictive accuracy measures and statistical tests. It suggests that the combination forecasting approach is superior to the individual one, and diversified information embedded in different explanatory variables should be integrated to improve tourism demand forecasting performance.
https://eprints.bournemouth.ac.uk/37934/
Source: Scopus
Does the combination of models With different explanatory variables improve tourism demand forecasting performance?
Authors: Wu, X. and Blake, A.
Journal: TOURISM ECONOMICS
Volume: 29
Issue: 8
Pages: 2032-2056
eISSN: 2044-0375
ISSN: 1354-8166
DOI: 10.1177/13548166221132645
https://eprints.bournemouth.ac.uk/37934/
Source: Web of Science (Lite)
Does the combination of models with different explanatory variables improve tourism demand forecasting performance?
Authors: Wu, X. and Blake, A.
Journal: Tourism Economics
Volume: 29
Issue: 8
Pages: 2032-2056
ISSN: 1354-8166
Abstract:The aim of this study is to assess whether combining econometric models with different explanatory variables can contribute to better tourism demand forecasts. Inbound tourism demand to the UK from seven leading markets is forecast, respectively, based on quarterly data using both individual and combination models. Causal econometric models that serve as constituents in combination take two specifications which are different in identified influencing factors. The empirical results show that generally including different explanatory variables in combination can produce better predictions according to both predictive accuracy measures and statistical tests. It suggests that the combination forecasting approach is superior to the individual one, and diversified information embedded in different explanatory variables should be integrated to improve tourism demand forecasting performance.
https://eprints.bournemouth.ac.uk/37934/
Source: BURO EPrints