Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility
研究了在宏观经济波动剧烈变化（如大缓和时期和近期衰退）的背景下，使用带随机波动的贝叶斯向量自回归模型对美国GDP增长、失业率、通胀和联邦基金利率进行实时密度预测，发现加入随机波动能显著提升预测准确性。
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.