动态因子随机波动-均值VAR用于大宏观经济面板
Dynamic Factor Stochastic Volatility-in-Mean VAR for Large Macroeconomic Panels
AI总结：
本文提出一种动态因子随机波动-均值VAR模型，通过引入波动性因素捕捉宏观经济波动，并在条件均值中纳入波动性，提升大面板预测性能。
摘要：
我们开发了一种动态因子随机波动-均值（SVM）规范，用于向量自回归（VAR）模型，其中在动态因子随机波动结构中嵌入了SVM组件。少量潜在波动因子捕捉了条件方差的共同运动，而波动性进入VAR的条件均值。这种规范允许时间变化的不确定性通过二阶矩和预期结果影响宏观经济动态，同时在大面板中保持可处理性。我们构建了高效的马尔可夫链蒙特卡洛算法进行估计，在高维、非高斯设置下实现高效估计。使用FRED-QD数据库中20个变量的季度数据，我们与基准随机波动VAR模型进行预测性能比较。动态因子SVM规范在主要宏观经济动荡期间（如2008年全球金融危机）对更多变量提供更优预测。结果表明，允许波动性进入均值捕捉了宏观经济动态中的一个重要传导渠道。
英文摘要：
We develop a dynamic factor stochastic volatility-in-mean (SVM) specification for vector autoregressions (VARs) that embeds an SVM component within a dynamic factor stochastic volatility structure. A small number of latent volatility factors capture common movements in conditional variances, while volatility enters the conditional mean of the VAR. This specification allows time-varying uncertainty to influence macroeconomic dynamics through both second moments and expected outcomes while preserving tractability in large panels. We construct an efficient Markov chain Monte Carlo algorithm for estimation in this high-dimensional, non-Gaussian setting. Using quarterly data on twenty variables from the FRED-QD database, we compare predictive performance with the benchmark stochastic volatility VAR model. The dynamic factor SVM specification delivers superior forecasts for more variables during major macroeconomic disruptions such as the 2008 global financial crisis. The results indicate that allowing volatility to enter the mean captures an important transmission channel in macroeconomic dynamics.
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