Bayesian Modeling of Time-Varying Parameters Using Regression Trees

Niko Hauzenberger, Florian Huber, Gary Koop and James Mitchell

No 23-05, Working Papers from Federal Reserve Bank of Cleveland

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). The novelty of this model stems 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. In contrast to other nonparametric and machine learning methods that are black box, inference using our model is straightforward because, in treating the parameters rather than the variables nonparametrically, the model remains conditionally linear in the mean. 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 inflationary measures vary nonlinearly with movements in uncertainty.

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 E32 (search for similar items in EconPapers)
Pages: 47
Date: 2023-01-11
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ets
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DOI: 10.26509/frbc-wp-202305

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