Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks

Florian Huber and Gary Koop

No 2309, Working Papers from University of Strathclyde Business School, Department of Economics

Abstract: The shocks which hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non-Gaussian, exhibiting asymmetries and fat tails. This consideration motivates the VAR developed in this paper which uses a Dirichlet process mixture (DPM) to model the shocks. However, we do not follow the obvious strategy of simply modelling the VAR errors with a DPM since this would lead to computationally infeasible Bayesian inference in larger VARs and potentially a sensitivity to the way the variables are ordered in the VAR. Instead we develop a particular additive error structure inspired by Bayesian nonparametric treatments of random effects in panel data models. We show that this leads to a model which allows for computationally fast and order-invariant inference in large VARs with nonparametric shocks. Our empirical results with nonparametric VARs of various dimensions shows that nonparametric treatment of the VAR errors is particularly useful in periods such as the financial crisis and the pandemic.

Keywords: Bayesian VARs; infinite mixtures; fast estimation; Markov chain Monte Carlo. (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 (search for similar items in EconPapers)
Pages: pages
Date: 2023-05
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Related works:
Journal Article: Fast and order‐invariant inference in Bayesian VARs with nonparametric shocks (2024)
Working Paper: Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:str:wpaper:2309

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