18 citations · 26 across the 10 of their papers we have counts for
12 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…
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 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…
Large Order-Invariant Bayesian VARs with Stochastic Volatility
Joshua C. C. Chan, Gary Koop, Xuewen Yu
Many popular specifications for Vector Autoregressions (VARs) with multivariate stochastic volatility are not invariant to the way the variables are ordered due to the use of a Cho…
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…
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…
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…
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…
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…
Inducing Sparsity and Shrinkage in Time-Varying Parameter Models
Florian Huber, Gary Koop, Luca Onorante
Time-varying parameter (TVP) models have the potential to be over-parameterized, particularly when the number of variables in the model is large. Global-local priors are increasing…