Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/318498 
Year of Publication: 
2024
Series/Report no.: 
WIFO Working Papers No. 690
Publisher: 
Austrian Institute of Economic Research (WIFO), Vienna
Abstract: 
Commonly used priors for Vector Autoregressions (VARs) induce shrinkage on the autoregressive coefficients. Introducing shrinkage on the error covariance matrix is sometimes done but, in the vast majority of cases, without considering the network structure of the shocks and by placing the prior on the lower Cholesky factor of the precision matrix. In this paper, we propose a prior on the VAR error precision matrix directly. Our prior, which resembles a standard spike and slab prior, models variable inclusion probabilities through a stochastic block model that clusters shocks into groups. Within groups, the probability of having relations across group members is higher (inducing less sparsity) whereas relations across groups imply a lower probability that members of each group are conditionally related. We show in simulations that our approach recovers the true network structure well. Using a US macroeconomic data set, we illustrate how our approach can be used to cluster shocks together and that this feature leads to improved density forecasts.
Subjects: 
Bayesian VAR
Stochastic Block Model
Stochastic search
Markov chain Monte Carlo
JEL: 
C11
C15
C30
C32
Document Type: 
Working Paper

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