Statistical properties of parametric estimators for Markov chain vectors based on copula models |
| |
Authors: | Wende Yi Stephen Shaoyi Liao |
| |
Institution: | 1. School of Economics & Management, Southwest Jiaotong University, Chengdu, Sichuan 610031, China;2. Department of Mathematics & Statistics, Chongqing University of Arts and Sciences Yongchuan Chongqing 402160, China;3. Department of Information Systems, City University of Hong Kong, Kowloon, Hong Kong |
| |
Abstract: | To estimate and measure risks, two key classes of dependence relationship must be identified: temporal dependence and contemporaneous dependence. In this paper, we propose a parametric estimation model that uses a three-stage pseudo maximum likelihood estimation (3SPMLE), and we investigate the consistency and asymptotic normality of parametric estimators. The proposed model combines the concept of a copula and the methods of parametric estimators of two-stage pseudo maximum likelihood estimation (2SPMLE). The selection of a copula model that best captures the dependence structure is a critical problem. To solve this problem, we propose a model selection method that is based on the parametric pseudo-likelihood ratio under the 3SPMLE for stationary Markov vector-type models. |
| |
Keywords: | Copula Asymptotic normality Temporal dependence Contemporaneous dependence 3SPMLE |
本文献已被 ScienceDirect 等数据库收录! |
|