Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility

TLDR

Central banks and forecasters increasingly rely on density forecasts, yet recent macroeconomic volatility spikes, such as those during the Great Moderation and the recent energy‑price‑driven turbulence, challenge their accuracy. This paper examines real‑time density forecasts of U.S. GDP growth, unemployment, inflation, and the federal funds rate using Bayesian vector autoregressions with stochastic volatility. The authors use real‑time data in Bayesian vector autoregressions that incorporate stochastic volatility to generate the forecasts. Adding stochastic volatility to BVARs materially improves the real‑time accuracy of density forecasts.

Abstract

Central banks and other forecasters are increasingly interested in various aspects of density forecasts. However, recent sharp changes in macroeconomic volatility, including the Great Moderation and the more recent sharp rise in volatility associated with increased variation in energy prices and the deep global recession—pose significant challenges to density forecasting. Accordingly, this paper examines, with real-time data, density forecasts of U.S. GDP growth, unemployment, inflation, and the federal funds rate from Bayesian vector autoregression (BVAR) models with stochastic volatility. The results indicate that adding stochastic volatility to BVARs materially improves the real-time accuracy of density forecasts. This article has supplementary material online.