Vector autoregressive models have widely been applied in macroeconomics and macroeconometrics to estimate economic relationships and to empirically assess theoretical hypothesis. To achieve the latter, we propose a Bayesian inference approach to analyze the dynamic interactions among macroeconomics variables in a graphical vector autoregressive model. The method decomposes the structural model into multivariate autoregressive and contemporaneous networks that can be represented in the form of a directed acyclic graph. We then simulated the networks with an independent sampling scheme based on a single-move Markov Chain Monte Carlo (MCMC) approach. We evaluated the efficiency of our inference procedure with a synthetic data and an empirical assessment of the business cycles hypothesis.
|Titolo:||Bayesian Graphical Models for Structural Vector Autoregressive Processes|
|Data di pubblicazione:||2012|
|Appare nelle tipologie:||3.1 Articolo su libro|
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|SSRN-id2198844.pdf||Documento in Pre-print||Accesso chiuso-personale||Riservato|