Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/333567 
Year of Publication: 
2025
Series/Report no.: 
WIFO Working Papers No. 714
Publisher: 
Austrian Institute of Economic Research (WIFO), Vienna
Abstract: 
Model misspecification in multivariate econometric models can strongly influence estimates of quantities of interest such as structural parameters, forecast distributions or responses to structural shocks, even more so if higher-order forecasts or responses are considered, due to parameter convolution. We propose a simple method for addressing these specification issues in the context of Bayesian VARs. Our method, called coarsened Bayesian VARs (cBVARs), replaces the exact likelihood with a coarsened likelihood that takes into account that the model might be misspecified along important but unknown dimensions. Since endogenous variables in a VAR can feature different degrees of misspecification, our model allows for this and automatically detects the degree of misspecification. The resulting cBVARs perform well in simulations for several types of misspecification. Applied to US data, cBVARs improve point and density forecasts compared to standard BVARs.
Subjects: 
Approximate inference
Asymmetric Conjugate Prior
Bayesian VARs
likelihood tempering
JEL: 
C11
C32
C53
Document Type: 
Working Paper

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