We document the impact of COVID-19 on inflation modelling within a vector autoregression
(VAR) model and provide guidance for forecasting euro area inflation during the pandemic …

While there exists numerous studies on the macroeconomic effects of oil and commodity
shocks, the literature is quite silent on the impact of macroeconomic uncertainty on oil and …

Many popular specifications for Vector Autoregressions (VARs) with multivariate stochastic
volatility are not invariant to the way the variables are ordered due to the use of a lower …

A class of global-local hierarchical shrinkage priors for estimating large Bayesian vector
autoregressions (BVARs) has recently been proposed. We question whether three such …

We introduce a class of large Bayesian vector autoregressions (BVARs) that allows for non-
Gaussian, heteroscedastic, and serially dependent innovations. To make estimation …

We document the impact of COVID-19 on frequently employed time series models, with a
focus on euro area inflation. We show that for both single equation models (Phillips curves) …

We propose importance sampling algorithms based on fast band matrix routines for
estimating the observed-data likelihoods for a variety of stochastic volatility models. This is …

Output growth estimates for regions of the UK are currently published at an annual
frequency only, released with a long delay, and offer limited historical coverage. To improve …

This paper uses a data rich environment to produce direct econometric estimates of
macroeconomic and financial uncertainty for 11 advanced nations. These indices exhibit …

We propose a novel and numerically efficient quantification approach to forecast uncertainty
of the real price of oil using a combination of probabilistic individual model forecasts. Our …