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,...

Full description

Bibliographic Details
Main Authors: Hong Li, Yuantao Xie, Juan Yang, Di Wang
Format: Article
Language:English
Published: Hindawi Limited 2018-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2018/5840296
id doaj-abd9b752a1614c3fa94f1a013b20565a
record_format Article
spelling 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
_version_ 1725211324130000896