This chapter reviews Bayesian methods for inference and forecasting with VAR models.
Bayesian inference and, by extension, forecasting depends on numerical methods for …

Bayesian forecasting is a natural product of a Bayesian approach to inference. The
Bayesian approach in general requires explicit formulation of a model, and conditioning on …

This paper proposes an analytical framework to quantify the impacts of climate policy and
transition narratives on economic and financial variables necessary for financial risk …

Abstract The Bayesian Estimation, Analysis and Regression toolbox (BEAR) is a
comprehensive (Bayesian)(Panel) VAR toolbox for forecasting and policy analysis. BEAR is …

This paper critically reviews the use of vector autoregressions (VARs) for four tasks: data
description, forecasting, structural inference, and policy analysis. The paper begins with a …

Multivariate simultaneous equations models were used extensively for macroeconometric
analysis when Sims (1980) advocated vector autoregressive (VAR) models as alternatives …

We address some of the key questions that arise in forecasting the price of crude oil. What
do applied forecasters need to know about the choice of sample period and about the …

The impact of announcements of large-scale purchases of government bonds on real GDP
and the CPI in the United Kingdom and the United States is explored with a Bayesian VAR …

This article examines the macroeconomic impact of the first round of quantitative easing
(QE) by the Bank of England. We attempt to quantify the effects of these purchases by …

The last twenty years have witnessed tremendous advances in the mathematical, statistical,
and computational tools available to applied macroeconomists. This rapidly evolving field …