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dc.contributor.authorPathairat Pastpipatkulen_US
dc.contributor.authorNisit Panthamiten_US
dc.contributor.authorWoraphon Yamakaen_US
dc.contributor.authorSongsak Sriboochittaen_US
dc.date.accessioned2018-09-05T02:57:57Z-
dc.date.available2018-09-05T02:57:57Z-
dc.date.issued2016-01-01en_US
dc.identifier.issn16113349en_US
dc.identifier.issn03029743en_US
dc.identifier.other2-s2.0-85005990012en_US
dc.identifier.other10.1007/978-3-319-49046-5_41en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85005990012&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/55566-
dc.description.abstract© Springer International Publishing AG 2016. This paper conducted a Markov switching seemingly unrelated regression without assuming a normal distribution of the error term. We proposed the use of both Archimedean and Elliptical copula classes to join the different marginal of the system equations. The results show that normal distribution for both demand and supply equations and joint distribution by Frank copulas present the lowest AIC and BIC. Moreover, the model is, then, applied for estimating the demand and supply in Thai sugar market. Thai export price and Brazil’s export price were found to be the factors affecting the demand and supply of the Thai sugar market. Finally, the results on smoothed probabilities indicate the oversupply condition in Thai sugar market along our sample period.en_US
dc.subjectComputer Scienceen_US
dc.subjectMathematicsen_US
dc.titleA copula-based markov switching seemingly unrelated regression approach for analysis the demand and supply on sugar marketen_US
dc.typeBook Seriesen_US
article.title.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
article.volume9978 LNAIen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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