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

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

© 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 t...

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Main Authors: Min N., Sriboonchitta S., Ramos V.
Format: Book Series
Published: 2017
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84952683740&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/42513
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-425132017-09-28T04:27:32Z Nonlinear estimations of tourist arrivals to Thailand: forecasting tourist arrivals by using SETAR models and STAR models Min N. Sriboonchitta S. Ramos V. © 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. 2017-09-28T04:27:32Z 2017-09-28T04:27:32Z 2016-01-01 Book Series 1860949X 2-s2.0-84952683740 10.1007/978-3-319-27284-9_26 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84952683740&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/42513
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
description © 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.
format Book Series
author Min N.
Sriboonchitta S.
Ramos V.
spellingShingle Min N.
Sriboonchitta S.
Ramos V.
Nonlinear estimations of tourist arrivals to Thailand: forecasting tourist arrivals by using SETAR models and STAR models
author_facet Min N.
Sriboonchitta S.
Ramos V.
author_sort Min N.
title Nonlinear estimations of tourist arrivals to Thailand: forecasting tourist arrivals by using SETAR models and STAR models
title_short Nonlinear estimations of tourist arrivals to Thailand: forecasting tourist arrivals by using SETAR models and STAR models
title_full Nonlinear estimations of tourist arrivals to Thailand: forecasting tourist arrivals by using SETAR models and STAR models
title_fullStr Nonlinear estimations of tourist arrivals to Thailand: forecasting tourist arrivals by using SETAR models and STAR models
title_full_unstemmed Nonlinear estimations of tourist arrivals to Thailand: forecasting tourist arrivals by using SETAR models and STAR models
title_sort nonlinear estimations of tourist arrivals to thailand: forecasting tourist arrivals by using setar models and star models
publishDate 2017
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84952683740&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/42513
_version_ 1681422203630911488

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