Fast and order-invariant inference in Bayesian VARs with non-parametric shocks
Huber, Florian and Koop, Gary (2024) Fast and order-invariant inference in Bayesian VARs with non-parametric shocks. Journal of Applied Econometrics. ISSN 0883-7252 (In Press)
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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 modeling 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.
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Item type: Article ID code: 89835 Dates: DateEvent4 July 2024Published4 July 2024AcceptedSubjects:
Social Sciences > Economic Theory > Methodology > Mathematical economics. Quantitative methods > EconometricsDepartment: Strathclyde Business School > Economics Depositing user: Pure Administrator Date deposited: 04 Jul 2024 09:44 Last modified: 04 Jul 2024 14:40 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/89835