General Bayesian time‐varying parameter vector autoregressions for modeling government bond yields. (5th October 2022)

• Record Type: Journal Article
• Authors: Fischer, Manfred M.; Hauzenberger, Niko; Huber, Florian; Pfarrhofer, Michael
• Is Part Of: Journal of applied econometrics. Volume 38:Number 1(2023)
• Page Start: 69
• Page End: 87
• Publication Date: 2022-10-05
• Subjects: Bayesian shrinkage -- interest rate forecasting -- latent effect modifiers -- MCMC sampling; Econometrics -- Periodicals; 330.015195
• DOI: 10.1002/jae.2936
• Languages: English
• ISSNs: 0883-7252

Abstract

Summary: 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.