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

Zichen Deng

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

Preview Full PDF BiBTex 66 188
  • 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.

  • History

Published online: 2012-03

  • AMS Subject Headings

62F12

  • Cited by