1 citations · 1 across the 2 of their papers we have counts for
6 papers
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…
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…
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…
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…
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…