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

Manfred M. Fischer, Niko Hauzenberger, Florian Huber, Michael Pfarrhofer

Publikation: Wissenschaftliche FachzeitschriftOriginalbeitrag in FachzeitschriftBegutachtung

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.
OriginalspracheEnglisch
FachzeitschriftJournal of Applied Econometrics
DOIs
PublikationsstatusVeröffentlicht - 2022

Österreichische Systematik der Wissenschaftszweige (ÖFOS)

  • 101018 Statistik
  • 502018 Makroökonomie

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