Cross country relations in European tourist arrivals. (March 2017)

• Record Type: Journal Article
• Authors: Silva, Emmanuel Sirimal; Ghodsi, Zara; Ghodsi, Mansi; Heravi, Saeed; Hassani, Hossein
• Is Part Of: Annals of tourism research. Volume 63(2017)
• Page Start: 151
• Page End: 168
• Publication Date: 2017-03
• Subjects: Multivariate Singular Spectrum Analysis -- Leading indicators -- Tourist arrivals -- Demand -- Europe; Tourism -- Periodicals
• DOI: 10.1016/j.annals.2017.01.012
• Languages: English
• ISSNs: 0160-7383

Abstract

Highlights: We introduce a new approach to find leading indicators in the tourism industry. Multivariate Singular Spectrum Analysis is introduced for tourism forecasting. We find the existence of cross country relations in European tourist arrivals. The MSSA algorithm outperforms SSA, ARIMA and ETS techniques. 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.