论文标题
固定结构向量自回归模型的长期风险
Long Run Risk in Stationary Structural Vector Autoregressive Models
论文作者
论文摘要
本文介绍了固定时间序列的局部对统一/小型西格玛过程，其持久性和不可忽略的长期风险。此过程表示涉及不同时间尺度的未观察到的短期和长期组件模型中的固定长期组件。更具体地说，短期组件在日历时间中演变，而长期组件则以超长时间的比例演变。我们开发了具有未观察到的组件的单变量和多变量结构VAR（SVAR）模型的估计方法和长期预测方法，并揭示了无法始终估算某些长期参数的可能性。该方法通过蒙特卡洛研究和宏观经济数据的应用来说明。
This paper introduces a local-to-unity/small sigma process for a stationary time series with strong persistence and non-negligible long run risk. This process represents the stationary long run component in an unobserved short- and long-run components model involving different time scales. More specifically, the short run component evolves in the calendar time and the long run component evolves in an ultra long time scale. We develop the methods of estimation and long run prediction for the univariate and multivariate Structural VAR (SVAR) models with unobserved components and reveal the impossibility to consistently estimate some of the long run parameters. The approach is illustrated by a Monte-Carlo study and an application to macroeconomic data.
