论文标题
在结构线性模型中对非高斯维度的强大估计
Robust Estimation of the non-Gaussian Dimension in Structural Linear Models
论文作者
论文摘要
可能非基本SVARMA模型的统计识别需要结构错误：（i）是一个i.i.d过程，（ii）在组件之间是相互独立的，并且（iii）每个过程都必须是非高斯分布式的。因此，提供前两个要求，评估非高斯识别条件至关重要。我们通过将结构误差向量的非高斯维度与从降低形式误差的高阶光谱构建的矩阵的等级联系起来来解决这个问题。这使我们的建议鲁棒性稳健地对滞后多项式的根位置，并概括了针对因果结构VAR模型的限制情况设计的当前程序。仿真练习表明，我们的程序令人满意地估计了非高斯组件的数量。
Statistical identification of possibly non-fundamental SVARMA models requires structural errors: (i) to be an i.i.d process, (ii) to be mutually independent across components, and (iii) each of them must be non-Gaussian distributed. Hence, provided the first two requisites, it is crucial to evaluate the non-Gaussian identification condition. We address this problem by relating the non-Gaussian dimension of structural errors vector to the rank of a matrix built from the higher-order spectrum of reduced-form errors. This makes our proposal robust to the roots location of the lag polynomials, and generalizes the current procedures designed for the restricted case of a causal structural VAR model. Simulation exercises show that our procedure satisfactorily estimates the number of non-Gaussian components.
