Efficient posterior simulation for cointegrated models with priors on the cointegration space

G.M. Koop, R. Leon-Gonzalez, R. Strachan

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


A message coming out of the recent Bayesian literature on cointegration is that it is important to elicit a prior on the space spanned by the cointegrating vectors (as opposed to a particular identified choice for these vectors). In previous work, such priors have been found to greatly complicate computation. In this paper, we develop algorithms to carry out efficient posterior simulation in cointegration models. In particular, we develop a collapsed Gibbs sampling algorithm which can be used with just-identifed models and demonstrate that it has very large computational advantages relative to existing approaches. For over-identifed models, we develop a parameter-augmented Gibbs sampling algorithm and demonstrate that it also has attractive computational properties.
Original languageEnglish
Pages (from-to)224-242
Number of pages18
JournalEconometric Reviews
Issue number2
Publication statusPublished - 2 Mar 2010


  • Bayesian
  • collapsed Gibbs sampler
  • error correction model
  • Markov Chain Monte Carlo
  • parameter-augmentation
  • reduced rank regression

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