Michael Pfarrhofer

Michael Pfarrhofer

Assistant Professor

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.

This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of extreme observations, for instance those produced by the COVID-19 pandemic of 2020. This is due to their flexibility and ability to model outliers. In an application involving four major euro area countries, we find substantial improvements in nowcasting performance relative to a linear mixed frequency VAR.

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.

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.