General Bayesian time-varying parameter vector autoregressions for modeling government bond yields

Fischer, Manfred M. and Hauzenberger, Niko and Huber, Florian and Pfarrhofer, Michael (2023) General Bayesian time-varying parameter vector autoregressions for modeling government bond yields. Journal of Applied Econometrics, 38 (1). pp. 69-87. ISSN 0883-7252 (https://doi.org/10.1002/jae.2936)

[thumbnail of Fischer-etal-2023-JAE-General-Bayesian-time‐varying-parameter-vector-autoregressions-for-modeling-government-bond-yields]
Preview
 Text. Filename: Fischer_etal_2023_JAE_General_Bayesian_time_varying_parameter_vector_autoregressions_for_modeling_government_bond_yields.pdf
Final Published Version
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (2MB)| Preview

Abstract

US yield curve dynamics are subject to time-variation, but there is ambiguity about its precise form. This paper develops a vector autoregressive (VAR) model with time-varying parameters and stochastic volatility, which treats the nature of parameter dynamics as unknown. Coefficients can evolve according to a random walk, a Markov switching process, observed predictors, or depend on a mixture of these. To decide which form is supported by the data and to carry out model selection, we adopt Bayesian shrinkage priors. Our framework is applied to model the US yield curve. We show that the model forecasts well, and focus on selected in-sample features to analyze determinants of structural breaks in US yield curve dynamics.

ORCID iDs

Fischer, Manfred M., Hauzenberger, Niko ORCID logoORCID: https://orcid.org/0000-0002-2683-8421, Huber, Florian and Pfarrhofer, Michael;
CORE (COnnecting REpositories)