Michael Pfarrhofer

Michael Pfarrhofer

Assistant Professor

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

less than 1 minute read

Published:

“General Bayesian time-varying parameter vector autoregressions for modeling government bond yields” [DOI], with Manfred M. Fischer, Niko Hauzenberger and Florian Huber, on flexibly modeling structural breaks in the dynamic evolution of government bond yields is now out in print in the January/February 2023 issue of Journal of Applied Econometrics.

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.

You May Also Enjoy

Bayesian nonparametric methods for macroeconomic forecasting

less than 1 minute read

Published:

Here is a [draft] of our chapter “Bayesian nonparametric methods for macroeconomic forecasting” (joint work with Massimiliano Marcellino) prepared for the Handbook of Macroeconomic Forecasting which is edited by Mike Clements and Ana Galvao.

Nowcasting in a pandemic using non-parametric mixed frequency VARs

less than 1 minute read

Published:

“Nowcasting in a pandemic using non-parametric mixed frequency VARs” [DOI], with Florian Huber, Gary Koop, Luca Onorante and Josef Schreiner, on mixed-frequency econometrics for multivariate regression trees is now out in print in the January 2023 issue of Journal of Econometrics.