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

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 evo...

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Bibliographic Details
Published inJournal of applied econometrics (Chichester, England) Vol. 38; no. 1; pp. 69 - 87
Main Authors Fischer, Manfred M., Hauzenberger, Niko, Huber, Florian, Pfarrhofer, Michael
Format Journal Article
LanguageEnglish
Published Chichester Wiley Periodicals Inc 01.01.2023
Subjects
Ambiguity
Autoregressive models
Bayesian analysis
Bayesian shrinkage
Econometrics
interest rate forecasting
latent effect modifiers
MCMC sampling
Parameters
Random walk
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Summary: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.
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ISSN:0883-7252
1099-1255
DOI:10.1002/jae.2936