Bayesian Reference Analysis of Cointegration

Mattias Villani


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. A simple procedure based on the Gibbs sampler is used to obtain the posterior distribution of both the number of cointegrating relations and the form of those relations, together with accompanying error correcting dynamics. Simulated data are used for illustration and for discussing the well-known issue of local non-identification.

Key words: Cointegration, Bayesian inference, Estimation, Rank.

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