General Bayesian time-varying parameter VARs for predicting government bond yields

TL;DR

This paper introduces a flexible Bayesian TVP VAR model that incorporates latent covariates with different dynamics to improve forecasting of government bond yields and explain structural breaks.

Contribution

It proposes a novel TVP VAR framework with latent covariates and Bayesian shrinkage priors for model selection, enhancing dynamic modeling of bond yields.

Findings

Outperforms competing models in US interest rate forecasting

Effectively captures structural breaks in the yield curve

Provides insights into the sources of time variation in coefficients

Abstract

Time-varying parameter (TVP) regressions commonly assume that time-variation in the coefficients is determined by a simple stochastic process such as a random walk. While such models are capable of capturing a wide range of dynamic patterns, the true nature of time variation might stem from other sources, or arise from different laws of motion. In this paper, we propose a flexible TVP VAR that assumes the TVPs to depend on a panel of partially latent covariates. The latent part of these covariates differ in their state dynamics and thus capture smoothly evolving or abruptly changing coefficients. To determine which of these covariates are important, and thus to decide on the appropriate state evolution, we introduce Bayesian shrinkage priors to perform model selection. As an empirical application, we forecast the US term structure of interest rates and show that our approach performs well relative to a set of competing models. We then show how the model can be used to explain structural breaks in coefficients related to the US yield curve.