Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/55584
Title: Nonlinear estimations of tourist arrivals to Thailand: forecasting tourist arrivals by using SETAR models and STAR models
Authors: Nyo Min
Songsak Sriboonchitta
Vicente Ramos
Authors: Nyo Min
Songsak Sriboonchitta
Vicente Ramos
Keywords: Computer Science
Issue Date: 1-Jan-2016
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.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84952683740&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/55584
ISSN: 1860949X
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.