This survey features three parts. The first one reviews the most recent literature on the
relationship between domestic (ie, country‐specific) uncertainty and the business cycle, and …

The COVID-19 pandemic has led to enormous data movements that strongly affect
parameters and forecasts from standard Bayesian vector autoregressions (BVARs). To …

Uncertainty rises in recessions and falls in booms. But what is the causal relationship? We
construct cross-country panel data on stock market returns to proxy for first-and second …

We estimate a novel measure of global financial uncertainty (GFU) with a dynamic factor
framework that jointly models global, regional, and country‐specific factors. We quantify the …

We survey approaches to macroeconomic forecasting during the COVID‐19 pandemic. Due
to the unprecedented nature of the episode, there was greater dependence on information …

We propose a novel mixed-frequency dynamic factor model with time-varying parameters
and stochastic volatility for macroeconomic nowcasting and develop a fast estimation …

Bayesian vector autoregressions with stochastic volatility in both the conditional mean and
variance (SVMVARs) are widely used for studying the macroeconomic effects of uncertainty …

In this research, we examine the use of the Laney p'control chart and the application of test
rules to assess governmental interventions throughout the COVID‐19 pandemic and …

We propose a method to adjust for data outliers in Bayesian Vector Autoregressions
(BVARs), which allows for different outlier magnitudes across variables and rescales the …

This paper evaluates the real‐time forecast performance of alternative Bayesian
autoregressive (AR) and vector autoregressive (VAR) models for the Australian …