Semiparametric Estimation and Panel Data Clustering Analysis Based on D-Vine and C-Vine

Abstract EN

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.