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.
R. F. Engle and C. W. J. Granger, “Co-Integration and Error Correction: Representation, Estimation, and Testing,” Econometrica, Vol. 55, No. 2, 1987, pp. 251-276.
S. Johansen, “Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models,” Econometrica, Vol. 59, No. 6, 1991, pp. 1551- 1580.
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.
J. Ghosh and D. B. Dunson, “Default Prior Distributions and Efficient Posterior Computation in Bayesian Factor Analysis,” Journal of Computational and Graphical Statistics, Vol. 18, No. 2, 2009, pp. 306-320.
K. Cui and D. B. Dunson, “Generalized Dynamic Factor Models for Mixed-Measurement Time Series,” Journal of Computational and Graphical Statistics, 2012.
D. A. Dickey and W. A. Fuller, “Distribution of the Estimators for Autoregressive Time Series with a Unit Root,” Journal of the American Statistical Association, Vol. 74, No. 366, 1979, pp. 427-431.
K. Triantafyllopoulos and G. Montana, “Dynamic Modeling of Mean-Reverting Spreads for Statistical Arbitrage,” Computational Management Science, Vol. 8, No. 1-2, 2011, pp. 23-49.
