Template-Type: ReDIF-Paper 1.0
Author-Name: Kleibergen, F.R.
Author-Name-Last: Kleibergen
Author-Name-First: Frank
Author-Person: pkl31
Author-Name: Paap, R.
Author-Name-Last: Paap
Author-Name-First: Richard
Author-Person: ppa494
Title: Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration
Abstract: Cointegration occurs when the long run multiplier of a vector autoregressive model exhibits rank reduction. Priors and posteriors of the parameters of the cointegration model are therefore proportional to priors and posteriors of the long run multiplier given that it has reduced rank. Rank reduction of the long run multiplier is modelled using a decomposition resulting from its singular value decomposition. It specifies the long run multiplier matrix as the sum of a matrix that equals the product of the adjustment parameters and the cointegrating vectors, i.e. the cointegration specification, and a matrix that models the deviation from cointegration. Priors and posteriors for the parameters of the cointegration model are obtained by restricting the latter matrix to zero in the prior and posterior of the unrestricted long run multiplier. The special decomposition of the long run multiplier results in unique posterior densities. This theory leads to a complete Bayesian framework for cointegration analysis. It includes prior specification, simulation schemes for obtaining posterior distributions and determination of the cointegration rank via Bayes factors. We illustrate the analysis with several simulated series, the UK data of Hendry and Doornik (1994) and the Danish data of Johansen and Juselius (1990).
Creation-Date: 1998-07-02
File-URL: https://repub.eur.nl/pub/1551/1_feweco19980702144947.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 9821
Keywords: Bayes factors, Bayesian analysis of cointegration, Cointegration, posterior densities
Handle: RePEc:ems:eureir:1551