28 citations · 43 across the 10 of their papers we have counts for
12 papers
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…
Machine Learning Econometrics: Bayesian algorithms and methods
Dimitris Korobilis, Davide Pettenuzzo
As the amount of economic and other data generated worldwide increases vastly, a challenge for future generations of econometricians will be to master efficient algorithms for infe…
High-dimensional macroeconomic forecasting using message passing algorithms
Dimitris Korobilis
This paper proposes two distinct contributions to econometric analysis of large information sets and structural instabilities. First, it treats a regression model with time-varying…
Bayesian dynamic variable selection in high dimensions
Gary Koop, Dimitris Korobilis
This paper proposes a variational Bayes algorithm for computationally efficient posterior and predictive inference in time-varying parameter (TVP) models. Within this context we sp…
Exchange Rate Predictability in a Changing World
Joseph Byrne, Dimitris Korobilis, Pinho Ribeiro
An expanding literature articulates the view that Taylor rules are helpful in predicting exchange rates. In a changing world however, Taylor rule parameters may be subject to struc…