Bayesian Modeling of TVP-VARs Using Regression Trees

TL;DR

This paper introduces a flexible Bayesian nonparametric TVP-VAR model using regression trees to capture complex, nonlinear parameter changes in macroeconomic data, enhancing understanding of economic dynamics.

Contribution

It develops a novel nonparametric TVP-VAR framework with Bayesian additive regression trees, allowing for flexible modeling of parameter evolution driven by effect modifiers.

Findings

Effectively tracks the evolving Phillips curve.

Captures nonlinear effects of shocks on inflation.

Demonstrates improved modeling of macroeconomic dynamics.

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 inflation measures vary nonlinearly with changes in the effect modifiers.