Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/82464
Authors: 
Corander, Jukka
Villani, Mattias
Year of Publication: 
2004
Series/Report no.: 
Sveriges Riksbank Working Paper Series 171
Abstract: 
We introduce a Bayesian approach to model assessment in the class of graphical vector autoregressive (VAR) processes. Due to the very large number of model structures that may be considered, simulation based inference, such as Markov chain Monte Carlo, is not feasible. Therefore, we derive an approximate joint posterior distribution of the number of lags in the autoregression and the causality structure represented by graphs using a fractional Bayes approach. Some properties of the approximation are derived and our approach is illustrated on a four-dimensional macroeconomic system and five-dimensional air pollution data.
Subjects: 
Causality
Fractional Bayes
graphical models
lag length selection
vector autoregression
JEL: 
C11
C22
C52
Document Type: 
Working Paper

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