28 citations · 43 across the 10 of their papers we have counts for
8 papers · 1 filter
Forecasting Oil Prices Across the Distribution: A Quantile VAR Approach
Hilde C. Bjornland, Nicolas Hardy, Dimitris Korobilis
We develop a Quantile Bayesian Vector Autoregression (QBVAR) to forecast real oil prices across different quantiles of the conditional distribution. The model allows predictor effe…
Learning from crises: A new class of time-varying parameter VARs with observable adaptation
Nicolas Hardy, Dimitris Korobilis
We revisit macroeconomic time-varying parameter vector autoregressions (TVP-VARs), whose persistent coefficients may adapt too slowly to large, abrupt shifts such as those during m…
Exploring Monetary Policy Shocks with Large-Scale Bayesian VARs
Dimitris Korobilis
I introduce a high-dimensional Bayesian vector autoregressive (BVAR) framework designed to estimate the effects of conventional monetary policy shocks. The model captures structura…
Monitoring multicountry macroeconomic risk
Dimitris Korobilis, Maximilian Schröder
We propose a multicountry quantile factor augmeneted vector autoregression (QFAVAR) to model heterogeneities both across countries and across characteristics of the distributions o…
Agreed and Disagreed Uncertainty
Luca Gambetti, Dimitris Korobilis, John Tsoukalas +1
When agents' information is imperfect and dispersed, existing measures of macroeconomic uncertainty based on the forecast error variance have two distinct drivers: the variance of…
Probabilistic Quantile Factor Analysis
Dimitris Korobilis, Maximilian Schröder
This paper extends quantile factor analysis to a probabilistic variant that incorporates regularization and computationally efficient variational approximations. We establish throu…
A new algorithm for structural restrictions in Bayesian vector autoregressions
Dimitris Korobilis
A comprehensive methodology for inference in vector autoregressions (VARs) using sign and other structural restrictions is developed. The reduced-form VAR disturbances are driven b…
Bayesian Approaches to Shrinkage and Sparse Estimation
Dimitris Korobilis, Kenichi Shimizu
In all areas of human knowledge, datasets are increasing in both size and complexity, creating the need for richer statistical models. This trend is also true for economic data, wh…