论文标题
非线性前（背部）铸造和因果 - 非纳卡萨尔VAR模型的创新过滤
Nonlinear Fore(Back)casting and Innovation Filtering for Causal-Noncausal VAR Models
论文作者
论文摘要
我们表明，混合的因果 - 非可纳斯卡尔矢量自回归（VAR）过程满足了日历和反向时间的Markov属性。基于该属性，我们引入了远前和向后预测密度的封闭形式公式，以进行点和背景外样品外。背面公式用于调整预测间隔，以在难以估计尾部分位数时获得所需的覆盖率。引入了用于评估估计不确定性的预测间隔的置信度设置。我们还定义了混合因果 - 非核VAR模型的新的非线性过去依赖性创新，以进行脉冲响应函数分析。我们的方法是通过模拟和油价和实际GDP增长率的应用来说明的。
We show that the mixed causal-noncausal Vector Autoregressive (VAR) processes satisfy the Markov property in both calendar and reverse time. Based on that property, we introduce closed-form formulas of forward and backward predictive densities for point and interval forecasting and backcasting out-of-sample. The backcasting formula is used for adjusting the forecast interval to obtain a desired coverage level when the tail quantiles are difficult to estimate. A confidence set for the prediction interval is introduced for assessing the uncertainty due to estimation. We also define new nonlinear past-dependent innovations of mixed causal-noncausal VAR models for impulse response function analysis. Our approach is illustrated by simulations and an application to oil prices and real GDP growth rates.
