Volume 3, Issue 4
Statistical Properties of Semiparametric Estimators for Copula-Based Markov Chain Vectors Models.

WENDE YI AND B. STEPHEN SHAOYI LIAO

Int. J. Numer. Anal. Mod. B,3 (2012), pp. 371-387

Published online: 2012-03

Export citation
  • Abstract
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.
  • AMS Subject Headings

62F12

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{IJNAMB-3-371, author = {WENDE YI AND B. STEPHEN SHAOYI LIAO}, title = {Statistical Properties of Semiparametric Estimators for Copula-Based Markov Chain Vectors Models.}, journal = {International Journal of Numerical Analysis Modeling Series B}, year = {2012}, volume = {3}, number = {4}, pages = {371--387}, abstract = {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.}, issn = {}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/ijnamb/290.html} }
TY - JOUR T1 - Statistical Properties of Semiparametric Estimators for Copula-Based Markov Chain Vectors Models. AU - WENDE YI AND B. STEPHEN SHAOYI LIAO JO - International Journal of Numerical Analysis Modeling Series B VL - 4 SP - 371 EP - 387 PY - 2012 DA - 2012/03 SN - 3 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/ijnamb/290.html KW - Copula KW - Semiparametric estimation KW - Temporal dependence KW - Contemporaneous dependence KW - and 2SSPPMLE AB - 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.
WENDE YI AND B. STEPHEN SHAOYI LIAO. (2012). Statistical Properties of Semiparametric Estimators for Copula-Based Markov Chain Vectors Models.. International Journal of Numerical Analysis Modeling Series B. 3 (4). 371-387. doi:
Copy to clipboard
The citation has been copied to your clipboard