Koop, G.M. and Leon-Gonzalez, R. and Strachan, R. (2010) Efficient posterior simulation for cointegrated models with priors on the cointegration space. Econometric Reviews, 29 (2). pp. 224-242.Full text not available in this repository. (Request a copy from the Strathclyde author)
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.
|Keywords:||Bayesian, collapsed Gibbs sampler, error correction model, Markov Chain Monte Carlo, parameter-augmentation, reduced rank regression, Commerce, Economics and Econometrics|
|Subjects:||Social Sciences > Commerce|
|Department:||Strathclyde Business School > Economics|
|Depositing user:||Strathprints Administrator|
|Date Deposited:||18 Jan 2010 14:55|
|Last modified:||22 Mar 2017 10:12|