Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models

We propose a high-dimensional copula to model the dependence structure of the seemingly unrelated quantile regression. As the conventional model faces with the strong assumption of the multivariate normal distribution and the linear dependence structure, thus, we apply the multivariate exchangeable...

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Main Authors: Nachatchapong Kaewsompong, Paravee Maneejuk, Woraphon Yamaka
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/6746303
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author Nachatchapong Kaewsompong
Paravee Maneejuk
Woraphon Yamaka
author_facet Nachatchapong Kaewsompong
Paravee Maneejuk
Woraphon Yamaka
author_sort Nachatchapong Kaewsompong
collection DOAJ
description We propose a high-dimensional copula to model the dependence structure of the seemingly unrelated quantile regression. As the conventional model faces with the strong assumption of the multivariate normal distribution and the linear dependence structure, thus, we apply the multivariate exchangeable copula function to relax this assumption. As there are many parameters to be estimated, we consider the Bayesian Markov chain Monte Carlo approach to estimate the parameter interests in the model. Four simulation studies are conducted to assess the performance of our proposed model and Bayesian estimation. Satisfactory results from simulation studies are obtained suggesting the good performance and reliability of the Bayesian method used in our proposed model. The real data analysis is also provided, and the empirical comparison indicates our proposed model outperforms the conventional models in all considered quantile levels.
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institution Kabale University
issn 1076-2787
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publishDate 2020-01-01
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series Complexity
spelling doaj-art-7919b07fb6f54f6892c3cbf518043d792025-02-03T05:52:29ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/67463036746303Bayesian Estimation of Archimedean Copula-Based SUR Quantile ModelsNachatchapong Kaewsompong0Paravee Maneejuk1Woraphon Yamaka2Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Chiang Mai 50200, ThailandCenter of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Chiang Mai 50200, ThailandCenter of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Chiang Mai 50200, ThailandWe propose a high-dimensional copula to model the dependence structure of the seemingly unrelated quantile regression. As the conventional model faces with the strong assumption of the multivariate normal distribution and the linear dependence structure, thus, we apply the multivariate exchangeable copula function to relax this assumption. As there are many parameters to be estimated, we consider the Bayesian Markov chain Monte Carlo approach to estimate the parameter interests in the model. Four simulation studies are conducted to assess the performance of our proposed model and Bayesian estimation. Satisfactory results from simulation studies are obtained suggesting the good performance and reliability of the Bayesian method used in our proposed model. The real data analysis is also provided, and the empirical comparison indicates our proposed model outperforms the conventional models in all considered quantile levels.http://dx.doi.org/10.1155/2020/6746303
spellingShingle Nachatchapong Kaewsompong
Paravee Maneejuk
Woraphon Yamaka
Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models
Complexity
title Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models
title_full Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models
title_fullStr Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models
title_full_unstemmed Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models
title_short Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models
title_sort bayesian estimation of archimedean copula based sur quantile models
url http://dx.doi.org/10.1155/2020/6746303
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AT paraveemaneejuk bayesianestimationofarchimedeancopulabasedsurquantilemodels
AT woraphonyamaka bayesianestimationofarchimedeancopulabasedsurquantilemodels