Abstract
This chapter analyses different types of AR (autoregressive) and VAR (vector autoregressive) models with a latent variables or errors-in-variables (eiv) structure from a Bayesian perspective. A hierarchical prior distribution which imposes constraints in the form of tightness (or smoothness) on the lag distribution is assumed. To find the posterior distributions of the hyperparameters, a Gibbs sampling approach is proposed. All the full conditional distributions (f.c.d.s) which are necessary for the efficient numerical calculation of the posterior distribution are derived. First, the single and multiple tightness models are analysed and then the extension to a VAR model is given. The simulated posterior distribution also allows for the simulation of the predictive distribution.
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Polasek, W. (2000). Gibbs Sampling in B-VAR Models with Latent Variables. In: Heijmans, R.D.H., Pollock, D.S.G., Satorra, A. (eds) Innovations in Multivariate Statistical Analysis. Advanced Studies in Theoretical and Applied Econometrics, vol 36. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4603-0_9
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DOI: https://doi.org/10.1007/978-1-4615-4603-0_9
Publisher Name: Springer, Boston, MA
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