Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility
Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility
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DOI：
10.1198/jbes.2010.09248
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发表时间：
2011-07-01
影响因子：
3
通讯作者：
Clark, Todd E.
中科院分区：
文献类型：
--
作者：
Clark, Todd E.
中央银行和其他预测者对密度预测的各个方面越来越感兴趣。然而，最近宏观经济波动的急剧变化，包括大稳健以及最近与能源价格波动加剧和全球深度衰退相关的波动性急剧上升，对密度预测构成了重大挑战。因此，本文利用实时数据，研究了具有随机波动性的贝叶斯向量自回归 (BVAR) 模型对美国 GDP 增长、失业率、通货膨胀和联邦基金利率的密度预测。结果表明，在 BVAR 中添加随机波动性可显着提高密度预测的实时准确性。本文有在线补充材料。
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.