86 citations · 156 across the 36 of their papers we have counts for
39 papers
Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks
Florian Huber, Gary Koop
The shocks which hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non-Gaussian, exhibiting asymmetries and fat tails. This consideration moti…
Coarsened Bayesian VARs -- Correcting BVARs for Incorrect Specification
Florian Huber, Massimiliano Marcellino, Tobias Scheckel
Model misspecification in multivariate econometric models can strongly influence estimates of quantities of interest such as structural parameters, forecast distributions or respon…
A tale of two tails: 130 years of growth-at-risk
Martin Gächter, Elias Hasler, Florian Huber
We extend the existing growth-at-risk (GaR) literature by examining a long time period of 130 years in a time-varying parameter regression model. We identify several important insi…
Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions
Jan Prüser, Florian Huber
Modeling and predicting extreme movements in GDP is notoriously difficult and the selection of appropriate covariates and/or possible forms of nonlinearities are key in obtaining p…
Bayesian Forecasting in Economics and Finance: A Modern Review
Gael M. Martin, David T. Frazier, Worapree Maneesoonthorn +6
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem…
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…
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…
Forecasting US Inflation Using Bayesian Nonparametric Models
Todd E. Clark, Florian Huber, Gary Koop +1
The relationship between inflation and predictors such as unemployment is potentially nonlinear with a strength that varies over time, and prediction errors error may be subject to…
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…
Global martingale solutions for stochastic Shigesada-Kawasaki-Teramoto population models
Marcel Braukhoff, Florian Huber, Ansgar Jüngel
The existence of global nonnegative martingale solutions to cross-diffusion systems of Shigesada-Kawasaki-Teramoto type with multiplicative noise is proven. The model describes the…
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…
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…
Subspace Shrinkage in Conjugate Bayesian Vector Autoregressions
Florian Huber, Gary Koop
Macroeconomists using large datasets often face the choice of working with either a large Vector Autoregression (VAR) or a factor model. In this paper, we develop methods for combi…
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…
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…
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…
On the equivalence of pathwise mild and weak solutions for quasilinear SPDEs
Gaurav Dhariwal, Florian Huber, Alexandra Neamţu
The main goal of this work is to relate weak and pathwise mild solutions for parabolic quasilinear stochastic partial differential equations (SPDEs). Extending in a suitable way te…
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…
Inference in Bayesian Additive Vector Autoregressive Tree Models
Florian Huber, Luca Rossini
Vector autoregressive (VAR) models assume linearity between the endogenous variables and their lags. This assumption might be overly restrictive and could have a deleterious impact…
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…
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…