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

Fischer, Manfred M. ORCID: and Hauzenberger, Niko and Huber, Florian ORCID: and Pfarrhofer, Michael ORCID: (2021) General Bayesian time-varying parameter VARs for predicting government bond yields. Working Papers in Regional Science, 2021/01. WU Vienna University of Economics and Business, Vienna.


Download (677kB) | Preview


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.

Item Type: Paper
Keywords: Bayesian shrinkage, interest rate forecasting, latent effect modifers, MCMC sampling, time-varying parameter regression
Classification Codes: JEL C11, C30, E37, E43
Divisions: Departments > Sozioökonomie > Wirtschaftsgeographie und Geoinformatik
Depositing User: ePub Administrator
Date Deposited: 22 Feb 2021 14:27
Last Modified: 22 Feb 2021 15:25


View Item View Item


Downloads per month over past year

View more statistics