A Bayesian Approach to Modelling Graphical Vector Autoregressions
AbstractWe 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.
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Bibliographic InfoPaper provided by Sveriges Riksbank (Central Bank of Sweden) in its series Working Paper Series with number 171.
Length: 19 pages
Date of creation: 01 Oct 2004
Date of revision:
Publication status: Published in Journal of Time Series Analysis, 2005, pages 141-156.
Causality; Fractional Bayes; graphical models; lag length selection; vector autoregression;
Other versions of this item:
- Jukka Corander & Mattias Villani, 2006. "A Bayesian Approach to Modelling Graphical Vector Autoregressions," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(1), pages 141-156, 01.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
This paper has been announced in the following NEP Reports:
- NEP-ETS-2004-11-07 (Econometric Time Series)
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- Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2012.
"Bayesian Graphical Models for Structural Vector Autoregressive Processes,"
2012:36, Department of Economics, University of Venice "Ca' Foscari".
- Daniel Felix Ahelegbey & Paolo Giudici, 2014. "Hierarchical Graphical Models, With Application To Systemic Risk," DEM Working Papers Series 063, University of Pavia, Department of Economics and Management.
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