Cointegrated Models with Priors on the Cointegration Space
نویسندگان
چکیده
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 identi ed choice for these vectors). In this note, we discuss a sensible way of eliciting such a prior. Furthermore, we develop a collapsed Gibbs sampling algorithm to carry out e¢ cient posterior simulation in cointegration models. The computational advantages of our algorithm are most pronounced with our model, since the form of our prior precludes simple posterior simulation using conventional methods (e.g. a Gibbs sampler involves non-standard posterior conditionals). However, the theory we draw upon implies our algorithm will be more e¢ cient even than the posterior simulation methods which are used with identi ed versions of cointegration models. Gary Koop ([email protected]) is at the University of Strathclyde. Roberto León-González ([email protected]) and Rodney Strachan ([email protected]) are at the University of Leicester. Address for correspondence: Roberto León-González, Department of Economics University of Leicester, LE1 7RH, Leicester, United Kingdom
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Efficient Posterior Simulation for Cointegrated Models with Priors On the Cointegration Space
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 co...
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