具有独立非高斯输入的SVARMA模型的辨识与估计
Identification and Estimation of SVARMA models with Independent and Non-Gaussian Inputs
摘要：
研究了独立冲击和非高斯冲击驱动下的结构向量自回归滑动平均（SVARMA）模型的可辨识性。众所周知，如果不对参数施加进一步的识别限制，则无法识别由高斯误差驱动的SVARMA模型。即使在简化形式下，并假设稳定性和可逆性，向量自回归移动平均模型一般不需要确定某些参数矩阵是非奇异的。冲击的独立性和非高斯性被用来表明它们被识别为排列和缩放。通过这种方式，通常施加的识别限制变得可测试。此外，我们引入了一个最大似然估计的非高斯SVARMA模型是一致的，渐近正态分布。
英文摘要：
This paper analyzes identifiability properties of structural vector autoregressive moving average (SVARMA) models driven by independent and non-Gaussian shocks. It is well known, that SVARMA models driven by Gaussian errors are not identified without imposing further identifying restrictions on the parameters. Even in reduced form and assuming stability and invertibility, vector autoregressive moving average models are in general not identified without requiring certain parameter matrices to be non-singular. Independence and non-Gaussianity of the shocks is used to show that they are identified up to permutations and scalings. In this way, typically imposed identifying restrictions are made testable. Furthermore, we introduce a maximum-likelihood estimator of the non-Gaussian SVARMA model which is consistent and asymptotically normally distributed.
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