activity
20182021
most citedBayesian Inference in High-Dimensional Time-varying Parameter Models using Integrated Rotated Gaussian Approximations

7 citations · 9 across the 7 of their papers we have counts for

collaborators

15 papers

econ.EM2021

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

Manfred M. Fischer, Niko Hauzenberger, Florian Huber +1

Time-varying parameter (TVP) regressions commonly assume that time-variation in the coefficients is determined by a simple stochastic process such as a random walk. While such mode…

econ.GN2020

On the effectiveness of the European Central Bank's conventional and unconventional policies under uncertainty

Niko Hauzenberger, Michael Pfarrhofer, Anna Stelzer

In this paper, we investigate the effectiveness of conventional and unconventional monetary policy measures by the European Central Bank (ECB) conditional on the prevailing level o…

econ.EM2020

Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs

Florian Huber, Gary Koop, Luca Onorante +2

This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees. We argue that regression tree model…

econ.EM2020

Measuring the Effectiveness of US Monetary Policy during the COVID-19 Recession

Martin Feldkircher, Florian Huber, Michael Pfarrhofer

The COVID-19 recession that started in March 2020 led to an unprecedented decline in economic activity across the globe. To fight this recession, policy makers in central banks eng…

econ.EM2020

Dynamic shrinkage in time-varying parameter stochastic volatility in mean models

Florian Huber, Michael Pfarrhofer

Successful forecasting models strike a balance between parsimony and flexibility. This is often achieved by employing suitable shrinkage priors that penalize model complexity but a…

econ.EM20202 cited

Forecasts with Bayesian vector autoregressions under real time conditions

Michael Pfarrhofer

This paper investigates the sensitivity of forecast performance measures to taking a real time versus pseudo out-of-sample perspective. We use monthly vintages for the United State…