Nonlinear estimations of tourist arrivals to Thailand: forecasting tourist arrivals by using SETAR models and STAR models

• Main Authors: Nyo Min, Songsak Sriboonchitta, Vicente Ramos
• Format: Book Series
• Published: 2018
• Subjects: Computer Science
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84952683740&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/55584
• Institution: Chiang Mai University

© Springer International Publishing Switzerland 2016. The main objective of this study is to evaluate some alternatives to estimate tourism arrivals under the presence of structural changes in the sample size. Several specification of Self-exciting threshold autoregressive (SETAR) model and Smooth transition autoregressive (STAR) model, especially Logistic STAR (LSTAR) are estimated. Once the parameters are estimated, a one period out of sample forecasting is performed to evaluate the forecasting efficiency of the best specifications. The finding from the study is that the STAR model beats SETAR model slightly, and these two groups of models have forecast proficiency at least in the tourism field.