Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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 computing the posterior distribution of the number of cointegrating relations and distribution of the model parameters for a given number of relations are proposed, including an efficient Gibbs sampling approach where all inferences are determined from the same posterior sample. Simulated data are used to illustrate the procedures and for discussing the well-known issue of local nonidentification.The author thanks Luc Bauwens, Anant Kshirsagar, Peter Phillips, Herman van Dijk, four anonymous referees, and especially Daniel Thorburn for helpful comments. Financial support from the Swedish Council of Research in Humanities and Social Sciences (HSFR) grant F0582/1999 and Swedish Research Council (Vetenskapsrådet) grant 412-2002-1007 is gratefully acknowledged. The views expressed in this paper are solely the responsibility of the author and should not be interpreted as reflecting the views of the Executive Board of Sveriges Riksbank.