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 …

We construct risks around consensus forecasts of real GDP growth, unemployment, and
inflation. We find that risks are time-varying, asymmetric, and partly predictable. Tight …

This article provides guidance on how to evaluate and improve the forecasting ability of
models in the presence of instabilities, which are widespread in economic time series …

Modeling price risks is crucial for economic decision making in energy markets. Besides the
risk associated with a single price, the dependence structure of multiple prices is often …

Abstract The sentiment, or “Tonality”, extracted from the narratives that accompany Federal
Reserve economic forecasts (in the Greenbook) is strongly correlated with future economic …

This paper studies the joint dynamics of US inflation and a term structure of average inflation
predictions taken from the Survey of Professional Forecasters (SPF). We estimate these joint …

Abstract The US Survey of Professional Forecasters produces precise and timely point
forecasts for key macro‐economic variables. However, the accompanying density forecasts …

This paper presents empirical evidence on how judgmental adjustments affect the accuracy
of macroeconomic density forecasts. Judgment is defined as the difference between …

We use a variant of machine learning (ML) to forecast Australia's automobile gasoline
demand within an autoregressive and structural model. By comparing the outputs of various …