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

Manfred Fischer, Niko Hauzenberger, Florian Huber and Michael Pfarrhofer

Journal of Applied Econometrics, 2023, vol. 38, issue 1, 69-87

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.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.1002/jae.2936

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:japmet:v:38:y:2023:i:1:p:69-87

Ordering information: This journal article can be ordered from
http://www3.intersci ... e.jsp?issn=0883-7252

Access Statistics for this article

Journal of Applied Econometrics is currently edited by M. Hashem Pesaran

More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().