General Bayesian time-varying parameter vector autoregressions for modeling government bond yields
- Author(s)
- Manfred M. Fischer, Niko Hauzenberger, Florian Huber, Michael Pfarrhofer
- 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.
- Organisation(s)
- Department of Economics
- External organisation(s)
- Paris-Lodron Universität Salzburg, Wirtschaftsuniversität Wien (WU)
- Journal
- Journal of Applied Econometrics
- Volume
- 38
- Pages
- 69-87
- No. of pages
- 19
- ISSN
- 0883-7252
- DOI
- https://doi.org/10.1002/jae.2936
- Publication date
- 09-2022
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 502025 Econometrics, 502018 Macroeconomics
- Keywords
- ASJC Scopus subject areas
- Economics and Econometrics, Social Sciences (miscellaneous)
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/general-bayesian-timevarying-parameter-vector-autoregressions-for-modeling-government-bond-yields(a9090b22-3f3e-41ff-9326-e89025a0a19f).html