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Statistical Properties of Semiparametric Estimators for Copula-Based Markov Chain Vectors Models
Company: International Journal of Numerical Analysis and Modeling, Series B
Company Url: Click here to open
Year Of Publication: 2012
Month Of Publication: December
Resource Link: Click here to open
Pages: 371-387
Download Count: 0
View Count: 1154
Comment Num: 0
Language: English
Source: article
Who Can Read: Free
Date: 12-16-2012
Publisher: Administrator
Summary
This paper proposes a method for estimation of a class of copula-based semiparametric stationary Markov vector time series models, namely, the two-stage semiparametric pseudo
maximum likelihood estimation (2SSPPMLE). These Markov vector time series models are characterized by nonparametric marginal distributions and parametric copula functions of temporal and contemporaneous dependence, while the copulas capture two classes of dependence relationships of Markov time series. We provide simple estimators of marginal distribution and two classes of copulas parameters and establish their asymptotic properties following conclusions in Chen and Fan (2006) and some easily verifiable conditions. Moreover, we obtain the estimation of conditional moment and conditional quantile functions for the bivariate Markov time series model.
(volume 3, number 4)
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Author(s)
Liao, B. Stephen Shaoyi Sign in to follow this author
Yi, Wende Sign in to follow this author
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