结构线性模型中非高斯维度的稳健估计
Robust Estimation of the non-Gaussian Dimension in Structural Linear Models
AI总结：
本文提出一种稳健估计结构线性模型中非高斯维度的方法，通过将结构误差的非高斯维度与简化型误差高阶谱矩阵的秩相关联，克服了对滞后多项式根位置的敏感性，并推广了现有因果结构VAR模型的方法。
摘要：
对可能非基本的SVARMA模型进行统计识别要求结构误差：（i）为独立同分布过程，（ii）各分量之间相互独立，（iii）每个分量必须服从非高斯分布。因此，在前两个条件满足的前提下，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由简化型误差的高阶谱构建的矩阵的秩联系起来，来解决这一问题。这使得我们的方法对滞后多项式的根位置具有稳健性，并推广了当前为因果结构VAR模型这一受限情形设计的程序。模拟实验表明，我们的程序能够令人满意地估计非高斯分量的数量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
