Business cycle of international tourism demand in Thailand: A Markov-switching Bayesian Vector Error Correction model

• Main Authors: Woraphon Yamaka, Pathairat Pastpipatkul, Songsak Sriboonchitta
• Format: Conference Proceeding
• Published: 2018
• Subjects: Computer Science; Mathematics
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84958549543&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/54373
• Institution: Chiang Mai University

© Springer International Publishing Switzerland 2015. This paper uses the Markov-switching Bayesian Vector Error Correction model (MS-BVECM) model to estimate the long-run and short-run relation for Thailand's tourism demand from five major countries, namely Japan, Korea, China, Russia, and Malaysia. The empirical findings of this study indicate that there exist a long-run and some short-run relationships between these five countries. Additionally, we analyses the business cycle in a set of five major tourism sources of Thailand and find two different regimes, namely high tourist arrival regime and low tourist arrival regime. Secondary monthly data results of forecasting were used to forecast the tourism demand from five major countries from November 2014 to August 2015 based on the period from January 1997 to October 2014. The results show that Chinese, Russian, and Malaysian tourists played an important role in Thailand's tourism industry during the period from May 2010 to October 2014.