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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Roengchai Tansuchat | en_US |
dc.contributor.author | Woraphon Yamaka | en_US |
dc.date.accessioned | 2018-09-05T04:26:10Z | - |
dc.date.available | 2018-09-05T04:26:10Z | - |
dc.date.issued | 2018-01-01 | en_US |
dc.identifier.issn | 16113349 | en_US |
dc.identifier.issn | 03029743 | en_US |
dc.identifier.other | 2-s2.0-85043991914 | en_US |
dc.identifier.other | 10.1007/978-3-319-75429-1_31 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85043991914&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/58549 | - |
dc.description.abstract | © 2018, Springer International Publishing AG, part of Springer Nature. In this paper, we develop a Markov Switching autoregressive distributed lag (MS-ARDL) model in which short- and long-run nonlinearities are introduced. The model is used to investigate the import demand of Nigeria for parboiled rice from Thailand. We demonstrate that the model is estimable by Maximum likelihood estimator and then a reliable long-run inference can be achieved by bound testing regardless of the integration orders of the variables. Furthermore, we first examine the accuracy of the model using a simulation study, and then the salient features of the model are employed to investigate the Thai parboiled rice demand from Nigeria. | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Mathematics | en_US |
dc.title | Markov-Switching ARDL Modeling of Parboiled Rice Import Demand from Thailand | en_US |
dc.type | Book Series | en_US |
article.title.sourcetitle | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en_US |
article.volume | 10758 LNAI | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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