论文标题
使用高阶动力学对结构VARMA模型的识别和估计
Identification and estimation of Structural VARMA models using higher order dynamics
论文作者
论文摘要
我们使用来自高阶矩的信息来识别非高斯结构矢量自回归移动平均值（SVARMA）模型，可能是非渠道或非因果的模型，该模型通过频域标准，基于频率域标准，基于对向量线性过程的高阶光谱密度阵列的新表示。这允许根据高阶累积动力学确定确定滞后矩阵多项式的根的位置，并确定导致结构性冲击的模型误差的旋转，从而符号和排列。我们描述了全局和局部参数识别的足够条件，这些条件依赖于线性动力学以及结构性创新的有限顺序序列和组件独立条件。我们概括了先前的单变量分析，以开发渐近正常和有效的估计，从而在没有因果关系或可逆性的情况下进行特定的结构性冲击秩序，从而利用第二和非高斯高阶动态。使用真实和模拟数据探索了有限样本分布和数值方法的属性的引导程序近似值。
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.
