20 citations · 35 across the 29 of their papers we have counts for
33 papers
Are there asymmetries in euro area monetary policy transmission?
Michael Pfarrhofer, Anna Stelzer
We answer the question posed in the title with a nonlinear mixed-frequency vector autoregression, estimated with Bayesian additive regression trees. The model combines monthly macr…
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…
Large Bayesian VARs for Binary, Censored and Count Variables
Joshua C. C. Chan, Michael Pfarrhofer, Davide Pettenuzzo +2
We extend the vector autoregression (VAR) to model binary, censored, count and unrestricted variables jointly, treating missing observations of any type in the same way. Each restr…
Scenario Analysis with Multivariate Bayesian Machine Learning Models
Michael Pfarrhofer, Anna Stelzer
We present an econometric framework that adapts tools for scenario analysis, such as variants of conditional forecasts and generalized impulse responses, for use with dynamic nonpa…
Asymmetries in Financial Spillovers
Florian Huber, Karin Klieber, Massimiliano Marcellino +2
This paper analyzes nonlinearities in the international transmission of financial shocks originating in the US. To do so, we develop a flexible nonlinear multi-country model. Our f…
General Seemingly Unrelated Local Projections
Florian Huber, Christian Matthes, Michael Pfarrhofer
We develop a flexible framework for Bayesian estimation of impulse responses using Local Projections (LPs) with instrumental variables. It accommodates multiple shocks and instrume…
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…
Forecasting euro area inflation using a huge panel of survey expectations
Florian Huber, Luca Onorante, Michael Pfarrhofer
In this paper, we forecast euro area inflation and its main components using an econometric model which exploits a massive number of time series on survey expectations for the Euro…
Measuring Shocks to Central Bank Independence using Legal Rulings
Stefan Griller, Florian Huber, Michael Pfarrhofer
We investigate the consequences of legal rulings on the conduct of monetary policy. Several unconventional monetary policy measures of the European Central Bank have come under scr…
Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model
Todd E. Clark, Florian Huber, Gary Koop +2
We develop a Bayesian non-parametric quantile panel regression model. Within each quantile, the response function is a convex combination of a linear model and a non-linear functio…
Approximate Bayesian inference and forecasting in huge-dimensional multi-country VARs
Martin Feldkircher, Florian Huber, Gary Koop +1
Panel Vector Autoregressions (PVARs) are a popular tool for analyzing multi-country datasets. However, the number of estimated parameters can be enormous, leading to computational…
Modeling tail risks of inflation using unobserved component quantile regressions
Michael Pfarrhofer
This paper proposes methods for Bayesian inference in time-varying parameter (TVP) quantile regression (QR) models featuring conditional heteroskedasticity. I use data augmentation…
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…
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…
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…
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…
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…
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…
Bayesian Inference in High-Dimensional Time-varying Parameter Models using Integrated Rotated Gaussian Approximations
Florian Huber, Gary Koop, Michael Pfarrhofer
Researchers increasingly wish to estimate time-varying parameter (TVP) regressions which involve a large number of explanatory variables. Including prior information to mitigate ov…
A multi-country dynamic factor model with stochastic volatility for euro area business cycle analysis
Florian Huber, Michael Pfarrhofer, Philipp Piribauer
This paper develops a dynamic factor model that uses euro area (EA) country-specific information on output and inflation to estimate an area-wide measure of the output gap. Our mod…
High-frequency and heteroskedasticity identification in multicountry models: Revisiting spillovers of monetary shocks
Michael Pfarrhofer, Anna Stelzer
We explore the international transmission of monetary policy and central bank information shocks originating from the United States and the euro area. Employing a panel vector auto…