7 citations · 16 across the 17 of their papers we have counts for
22 papers
Conditional projection methods for large-scale Bayesian VARs
Niko Hauzenberger, Michael Pfarrhofer
We develop methods for conditional forecasting and structural scenario analysis with high-dimensional Bayesian vector autoregressions (VARs). Our framework features a factor struct…
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…
Nowcasting with Mixed Frequency Data Using Gaussian Processes
Niko Hauzenberger, Massimiliano Marcellino, Michael Pfarrhofer +1
We develop Bayesian machine learning methods for mixed data sampling (MIDAS) regressions. This involves handling frequency mismatches and specifying functional relationships betwee…
Nowcasting economic activity in European regions using a mixed-frequency dynamic factor model
Luca Barbaglia, Lorenzo Frattarolo, Niko Hauzenberger +5
Timely information about the state of regional economies can be essential for planning, implementing and evaluating locally targeted economic policies. However, European regional a…
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…
Bayesian Neural Networks for Macroeconomic Analysis
Niko Hauzenberger, Florian Huber, Karin Klieber +1
Macroeconomic data is characterized by a limited number of observations (small T), many time series (big K) but also by featuring temporal dependence. Neural networks, by contrast,…
Bayesian Modeling of TVP-VARs Using Regression Trees
Niko Hauzenberger, Florian Huber, Gary Koop +1
In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric T…
Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty
Niko Hauzenberger, Florian Huber, Massimiliano Marcellino +1
We develop a non-parametric multivariate time series model that remains agnostic on the precise relationship between a (possibly) large set of macroeconomic time series and their l…
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…
Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques
Niko Hauzenberger, Florian Huber, Karin Klieber
In this paper, we assess whether using non-linear dimension reduction techniques pays off for forecasting inflation in real-time. Several recent methods from the machine learning l…
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…
Sparse time-varying parameter VECMs with an application to modeling electricity prices
Niko Hauzenberger, Michael Pfarrhofer, Luca Rossini
In this paper we propose a time-varying parameter (TVP) vector error correction model (VECM) with heteroskedastic disturbances. We propose tools to carry out dynamic model specific…
Capital Flows and the Stabilizing Role of Macroprudential Policies in CESEE
Markus Eller, Niko Hauzenberger, Florian Huber +2
In line with the recent policy discussion on the use of macroprudential measures to respond to cross-border risks arising from capital flows, this paper tries to quantify to what e…
Flexible Mixture Priors for Large Time-varying Parameter Models
Niko Hauzenberger
Time-varying parameter (TVP) models often assume that the TVPs evolve according to a random walk. This assumption, however, might be questionable since it implies that coefficients…
Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods
Niko Hauzenberger, Florian Huber, Gary Koop
Time-varying parameter (TVP) regression models can involve a huge number of coefficients. Careful prior elicitation is required to yield sensible posterior and predictive inference…
Combining Shrinkage and Sparsity in Conjugate Vector Autoregressive Models
Niko Hauzenberger, Florian Huber, Luca Onorante
Conjugate priors allow for fast inference in large dimensional vector autoregressive (VAR) models but, at the same time, introduce the restriction that each equation features the s…
Bayesian state-space modeling for analyzing heterogeneous network effects of US monetary policy
Niko Hauzenberger, Michael Pfarrhofer
Understanding disaggregate channels in the transmission of monetary policy is of crucial importance for effectively implementing policy measures. We extend the empirical econometri…
Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models
Niko Hauzenberger, Florian Huber, Gary Koop +1
In this paper, we write the time-varying parameter (TVP) regression model involving K explanatory variables and T observations as a constant coefficient regression model with KT ex…
Model instability in predictive exchange rate regressions
Niko Hauzenberger, Florian Huber
In this paper we aim to improve existing empirical exchange rate models by accounting for uncertainty with respect to the underlying structural representation. Within a flexible Ba…
Stochastic model specification in Markov switching vector error correction models
Niko Hauzenberger, Florian Huber, Michael Pfarrhofer +1
This paper proposes a hierarchical modeling approach to perform stochastic model specification in Markov switching vector error correction models. We assume that a common distribut…
Implications of macroeconomic volatility in the Euro area
Niko Hauzenberger, Maximilian Böck, Michael Pfarrhofer +2
In this paper we estimate a Bayesian vector autoregressive model with factor stochastic volatility in the error term to assess the effects of an uncertainty shock in the Euro area.…