Cross country relations in European tourist arrivals
Authors: Silva, E.S., Ghodsi, Z., Ghodsi, M., Heravi, S. and Hassani, H.
Journal: Annals of Tourism Research
Volume: 63
Pages: 151-168
ISSN: 0160-7383
DOI: 10.1016/j.annals.2017.01.012
Abstract:This paper introduces an optimized Multivariate Singular Spectrum Analysis (MSS) algorithm for identifying leading indicators. Exploiting European tourist arrivals data, we analyse cross country relations for European tourism demand. Cross country relations have the potential to aid in planning and resource allocations for future tourism demand by taking into consideration the variation in tourist arrivals across other countries in Europe. Our findings indicate with statistically significant evidence that there exists cross country relations between European tourist arrivals which can help in improving the predictive accuracy of tourism demand. We also find that MSSA has the capability of not only identifying leading indicators, but also forecasting tourism demand with far better accuracy in comparison to its univariate counterpart, Singular Spectrum Analysis.
https://eprints.bournemouth.ac.uk/29716/
Source: Scopus
Cross country relations in European tourist arrivals
Authors: Silva, E.S., Ghodsi, Z., Ghodsi, M., Heravi, S. and Hassani, H.
Journal: ANNALS OF TOURISM RESEARCH
Volume: 63
Pages: 151-168
eISSN: 1873-7722
ISSN: 0160-7383
DOI: 10.1016/j.annals.2017.01.012
https://eprints.bournemouth.ac.uk/29716/
Source: Web of Science (Lite)
Cross country relations in European tourist arrivals
Authors: Silva, E.S., Ghodsi, Z., Ghodsi, M., Heravi, S. and Hassani, H.
Journal: ANNALS OF TOURISM RESEARCH
Volume: 63
Pages: 151-168
ISSN: 0160-7383
DOI: 10.1016/j.annals.2017.01.012
https://eprints.bournemouth.ac.uk/29716/
Source: Manual
Cross country relations in European tourist arrivals
Authors: Silva, E.S., Ghodsi, Z., Ghodsi, M., Heravi, S. and Hassani, H.
Journal: Annals of tourism research
Volume: 63
Issue: March
Pages: 151-168
ISSN: 0160-7383
Abstract:This paper introduces an optimized Multivariate Singular Spectrum Analysis (MSS) algorithm for identifying leading indicators. Exploiting European tourist arrivals data, we analyse cross country relations for European tourism demand. Cross country relations have the potential to aid in planning and resource allocations for future tourism demand by taking into consideration the variation in tourist arrivals across other countries in Europe. Our findings indicate with statistically significant evidence that there exists cross country relations between European tourist arrivals which can help in improving the predictive accuracy of tourism demand. We also find that MSSA has the capability of not only identifying leading indicators, but also forecasting tourism demand with far better accuracy in comparison to its univariate counterpart, Singular Spectrum Analysis.
https://eprints.bournemouth.ac.uk/29716/
Source: BURO EPrints