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

Publication: Scientific journalJournal articlepeer-review


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.
Original languageEnglish
Pages (from-to)69-87
JournalJournal of Applied Econometrics
Issue number1
Publication statusPublished - 2022

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 101018 Statistics
  • 502018 Macroeconomics

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