Bayesian modeling of TVP-VARs using regression trees. WP

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 arises from the law of motion driving the parameters being 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.

Federal Reserve Bank of Cleveland working paper.

arXiv.

Slides. Presented at Bayes@Austria 2020 (by co-author), CFE 2022 (by co-author), CFOBE 2022 (by co-author), Econometrics seminar series 2023 (University of Glasgow), FRBC forecasting group seminar (by co-author), IIASA research seminar (by co-author), Xiamen University economic research seminar (by co-author), CFE 2023.