Bayesian Factorized Cointegration Analysis

Bayesian Factorized Cointegration Analysis

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 ...

Bayesian Cointegration Analysis

This paper proposes Bayesian estimation of cointegrated VAR systems and a simple method of estimating the cointegration rank using the Bayes factors for the adjustment term. Monte Carlo experiments show that the method proposed is more powerful in selecting the rank, especially with a small sample size, than Johansen's LR test. This is due to the fact that Bayesian analysis uses the exact distr...

Bayesian Reference Analysis of Cointegration

A Bayesian reference analysis of the cointegrated vector autoregression is presented based on a new prior distribution. Among other properties, it is shown that this prior distribution distributes its probability mass uniformly over all cointegration spaces for a given cointegration rank and is invariant to the choice of normalizing variables for the cointegration vectors. Several methods for c...

Bayesian Conditional Cointegration

Cointegration is an important topic for timeseries, and describes a relationship between two series in which a linear combination is stationary. Classically, the test for cointegration is based on a two stage process in which first the linear relation between the series is estimated by Ordinary Least Squares. Subsequently a unit root test is performed on the residuals. A well-known deficiency o...

Bayesian Approaches to Cointegration