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G. Huerta and M. West. “Priors and Component Structures in Autoregressive Time Series Models,” Journal of the Royal Statistical Society Series B, Vol. 61, No. 4, 1999, pp. 881-899.

has been cited by the following article:

• TITLE: Bayesian Factorized Cointegration Analysis

  AUTHORS: Kai Cui, Wenshan Cui

  KEYWORDS: Cointegration; Bayesian; Dynamic Factor; Non-Stationary; Root Structure; MCMC

  JOURNAL NAME: Open Journal of Statistics, Vol.2 No.5, December 19, 2012

  ABSTRACT: The concept of cointegration is widely used in applied non-stationary time series analysis to describe the co-movement of data measured over time. In this paper, we proposed a Bayesian model for cointegration test and analysis, based on the dynamic latent factor framework. Efficient computational algorithms are also developed based on Markov Chain Monte Carlo (MCMC). Performance and efficiency of the the model and approaches are assessed by simulated and real data analysis.