4 citations · 5 across the 8 of their papers we have counts for
12 papers
Conditional projection methods for large-scale Bayesian VARs
Niko Hauzenberger, Michael Pfarrhofer
We develop fast methods for conditional forecasting and structural scenario analysis with high-dimensional Bayesian vector autoregressions (VARs). Our general framework features a…
A Bayesian Gaussian Process Dynamic Factor Model
Tony Chernis, Niko Hauzenberger, Haroon Mumtaz +1
We propose a dynamic factor model (DFM) where the latent factors are linked to observed variables with unknown and potentially nonlinear functions. The key novelty and source of fl…
Machine Learning the Macroeconomic Effects of Financial Shocks
Niko Hauzenberger, Florian Huber, Karin Klieber +1
We propose a method to learn the nonlinear impulse responses to structural shocks using neural networks, and apply it to uncover the effects of US financial shocks. The results rev…
Predictive Density Combination Using a Tree-Based Synthesis Function
Tony Chernis, Niko Hauzenberger, Florian Huber +2
Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of exist…
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…
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…