Bayesian analysis of the error correction model

摘要

This paper presents a method for estimating the posterior probability density of the cointegrat-ing rank of a multivariate error correction model. A second contribution is the careful elicitation of the prior for the cointegrating vectors derived froma prior on the cointegrating space. This prior obtains naturally from treating the cointegrating space as the parameter of interest in inference and overcomes problems previously encountered in Bayesian cointegration analysis. Using this new prior and Laplace approximation, an estimator for the posterior probability of the rank is given. The approach performs well compared with information criteria in Monte Carlo experiments.