Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorNyo Minen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.contributor.authorVicente Ramosen_US
dc.description.abstract© 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.en_US
dc.subjectComputer Scienceen_US
dc.titleNonlinear estimations of tourist arrivals to Thailand: forecasting tourist arrivals by using SETAR models and STAR modelsen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume622en_US Mai Universityen_US de les Illes Balearsen_US
Appears in Collections:CMUL: Journal Articles

Files in This Item:
There are no files associated with this item.

Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.