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

Author

Listed:
  • Fischer, Manfred M.
  • Hauzenberger, Niko
  • Huber, Florian
  • Pfarrhofer, Michael

Abstract

US yield curve dynamics are subject to time-variation, but there is ambiguity on its precise form. This paper develops a vector autoregressive 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 is supported by the data, we adopt Bayesian shrinkage priors to carry out model selection. 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.

Suggested Citation

  • Fischer, Manfred M. & Hauzenberger, Niko & Huber, Florian & Pfarrhofer, Michael, 2022. "General Bayesian time-varying parameter VARs for modeling government bond yields," Working Papers in Regional Science 2021/01, WU Vienna University of Economics and Business.
    • Handle: RePEc:wiw:wus046:8006
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    Cited by:

    1. Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2020. "Bayesian Modelling of TVP-VARs Using Regression Trees," Working Papers 2308, University of Strathclyde Business School, Department of Economics, revised Aug 2023.
    2. Hauzenberger, Niko & Pfarrhofer, Michael & Stelzer, Anna, 2021. "On the effectiveness of the European Central Bank’s conventional and unconventional policies under uncertainty," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 822-845.

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    Keywords

    Bayesian shrinkage; interest rate forecasting; latent effect modifers; MCMC sampling;
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