基于高阶动力学的结构VARMA模型辨识与估计
Identification and estimation of Structural VARMA models using higher order dynamics
摘要：
我们使用高阶矩的信息来实现非高斯结构向量自回归移动平均（SVARMA）模型的识别，可能是非基本或非因果的，通过一个频域标准的基础上的一个新的表示的高阶谱密度阵列的向量线性过程。这允许基于高阶累积量动态识别行列式滞后矩阵多项式的根的位置，并识别导致结构冲击的模型误差的旋转直到符号和排列。我们描述的充分条件的全球和局部参数识别依赖于简单的秩假设的线性动力学和有穷级序列和组件的独立性条件的结构创新。我们推广以前的单变量分析，开发渐近正常和有效估计利用第二和非高斯高阶动态给定一个特定的结构性冲击排序没有假设的因果关系或可逆性。有限样本分布的Bootstrap近似和数值方法的性质进行了探索与实际和模拟数据。
英文摘要：
We use information from higher order moments to achieve identification of non-Gaussian structural vector autoregressive moving average (SVARMA) models, possibly non-fundamental or non-causal, through a frequency domain criterion based on a new representation of the higher order spectral density arrays of vector linear processes. This allows to identify the location of the roots of the determinantal lag matrix polynomials based on higher order cumulants dynamics and to identify the rotation of the model errors leading to the structural shocks up to sign and permutation. We describe sufficient conditions for global and local parameter identification that rely on simple rank assumptions on the linear dynamics and on finite order serial and component independence conditions for the structural innovations. We generalize previous univariate analysis to develop asymptotically normal and efficient estimates exploiting second and non-Gaussian higher order dynamics given a particular structural shocks ordering without assumptions on causality or invertibility. Bootstrap approximations to finite sample distributions and the properties of numerical methods are explored with real and simulated data.
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