Semiparametric Estimation and Panel Data Clustering Analysis Based on D-Vine and C-Vine
This paper proposed a panel data clustering model based on D-vine and C-vine and supported a semiparametric estimation for parameters. These models include a two-step inference function for margins, two-step semiparameter estimation, and stepwise semiparametric estimation. In similarity measurement,...
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2018-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2018/5840296 |
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doaj-abd9b752a1614c3fa94f1a013b20565a2020-11-25T01:01:01ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/58402965840296Semiparametric Estimation and Panel Data Clustering Analysis Based on D-Vine and C-VineHong Li0Yuantao Xie1Juan Yang2Di Wang3School of Economics, Peking University, ChinaSchool of Insurance and Economics, University of International Business and Economics, ChinaInstitute of Comprehensive Development, Chinese Academy of Science and Technology for Development, ChinaSchool of Economics, Peking University, ChinaThis paper proposed a panel data clustering model based on D-vine and C-vine and supported a semiparametric estimation for parameters. These models include a two-step inference function for margins, two-step semiparameter estimation, and stepwise semiparametric estimation. In similarity measurement, similarity coefficients are constructed by a multivariate Hierarchical Nested Archimedean Copula (HNAC) model and compound PCC models, which are HNAC and D-vine compound model and HNAC and C-vine compound model. Estimation solutions and models evaluation are given for these models. In the case study, the clustering results of HNAC and D-vine compound model and HNAC and C-vine compound model are given, and the effect of different copula families on clustering results is also discussed. The result shows the models are effective and useful.http://dx.doi.org/10.1155/2018/5840296 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hong Li Yuantao Xie Juan Yang Di Wang |
spellingShingle |
Hong Li Yuantao Xie Juan Yang Di Wang Semiparametric Estimation and Panel Data Clustering Analysis Based on D-Vine and C-Vine Mathematical Problems in Engineering |
author_facet |
Hong Li Yuantao Xie Juan Yang Di Wang |
author_sort |
Hong Li |
title |
Semiparametric Estimation and Panel Data Clustering Analysis Based on D-Vine and C-Vine |
title_short |
Semiparametric Estimation and Panel Data Clustering Analysis Based on D-Vine and C-Vine |
title_full |
Semiparametric Estimation and Panel Data Clustering Analysis Based on D-Vine and C-Vine |
title_fullStr |
Semiparametric Estimation and Panel Data Clustering Analysis Based on D-Vine and C-Vine |
title_full_unstemmed |
Semiparametric Estimation and Panel Data Clustering Analysis Based on D-Vine and C-Vine |
title_sort |
semiparametric estimation and panel data clustering analysis based on d-vine and c-vine |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2018-01-01 |
description |
This paper proposed a panel data clustering model based on D-vine and C-vine and supported a semiparametric estimation for parameters. These models include a two-step inference function for margins, two-step semiparameter estimation, and stepwise semiparametric estimation. In similarity measurement, similarity coefficients are constructed by a multivariate Hierarchical Nested Archimedean Copula (HNAC) model and compound PCC models, which are HNAC and D-vine compound model and HNAC and C-vine compound model. Estimation solutions and models evaluation are given for these models. In the case study, the clustering results of HNAC and D-vine compound model and HNAC and C-vine compound model are given, and the effect of different copula families on clustering results is also discussed. The result shows the models are effective and useful. |
url |
http://dx.doi.org/10.1155/2018/5840296 |
work_keys_str_mv |
AT hongli semiparametricestimationandpaneldataclusteringanalysisbasedondvineandcvine AT yuantaoxie semiparametricestimationandpaneldataclusteringanalysisbasedondvineandcvine AT juanyang semiparametricestimationandpaneldataclusteringanalysisbasedondvineandcvine AT diwang semiparametricestimationandpaneldataclusteringanalysisbasedondvineandcvine |
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1725211324130000896 |