Bayesian Modelling of TVP-VARs Using Regression Trees

Niko Hauzenberger, Florian Huber, Gary Koop and James Mitchell
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Niko Hauzenberger: University of Salzburg

No 2308, Working Papers from University of Strathclyde Business School, Department of Economics

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 modifi ers. 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 inflation measures vary nonlinearly with changes in the effect modifiers.

Keywords: Bayesian vector autoregression; Time-varying parameters; Nonparametric modeling; Machine learning; Regression trees; Phillips curve; Business cycle shocks (search for similar items in EconPapers)
JEL-codes: C11 C32 C51 E31 E32 (search for similar items in EconPapers)
Pages: pages
Date: 2020-02, Revised 2023-08
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Working Paper: Bayesian Modeling of TVP-VARs Using Regression Trees (2023)
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