7 citations · 9 across the 12 of their papers we have counts for
24 papers · 1 filter
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 and Censored Variables
Joshua C. C. Chan, Michael Pfarrhofer
We extend the standard VAR to jointly model the dynamics of binary, censored and continuous variables, and develop an efficient estimation approach that scales well to high-dimensi…
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…