7 citations · 9 across the 4 of their papers we have counts for
6 papers
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…
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,…
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…
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…
Can Machine Learning Catch the COVID-19 Recession?
Philippe Goulet Coulombe, Massimiliano Marcellino, Dalibor Stevanovic
Based on evidence gathered from a newly built large macroeconomic data set for the UK, labeled UK-MD and comparable to similar datasets for the US and Canada, it seems the most pro…