Bayesian Modelling of TVP-VARs Using Regression Trees

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Hauzenberger, Niko and Huber, Florian and Koop, Gary and Mitchell, James (2023) Bayesian Modelling of TVP-VARs Using Regression Trees. Discussion paper. University of Strathclyde, Glasgow.

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Abstract

In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART) that models the TVPs as an unknown function of effect modifiers. The novelty of this model arises from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. Parsimony is achieved through adopting nonparametric factor structures and use of shrinkage priors. In an application to US macroeconomic data, we illustrate the use of our model in tracking both the evolving nature of the Phillips curve and how the effects of business cycle shocks on in ation measures vary nonlinearly with changes in the effect modifiers.

ORCID iDs

Hauzenberger, Niko ORCID logoORCID: https://orcid.org/0000-0002-2683-8421, Huber, Florian, Koop, Gary ORCID logoORCID: https://orcid.org/0000-0002-6091-378X and Mitchell, James;
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