摘要翻译：
将向量自回归模型(VAR)结构可辨识性的已知结果推广到新息协方差矩阵降秩的情形。例如，结构奇异VAR模型作为理性预期模型的解出现，在理性预期模型中，冲击的数量通常小于内生变量的数量，并且作为动态因素模型的一个基本组成部分。我们证明了可辨识性的序条件在奇异情况下是误导性的，并给出了噪声参数可辨识性的秩条件。由于Yule-Walker方程可能存在多解，我们详细分析了系统参数限制对过辨识和欠辨识的影响，并给出了易于验证的条件。
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英文标题：
《Identifiability of Structural Singular Vector Autoregressive Models》
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作者：
Bernd Funovits, Alexander Braumann
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  We generalize well-known results on structural identifiability of vector autoregressive models (VAR) to the case where the innovation covariance matrix has reduced rank. Structural singular VAR models appear, for example, as solutions of rational expectation models where the number of shocks is usually smaller than the number of endogenous variables, and as an essential building block in dynamic factor models. We show that order conditions for identifiability are misleading in the singular case and provide a rank condition for identifiability of the noise parameters. Since the Yule-Walker equations may have multiple solutions, we analyze the effect of restrictions on the system parameters on over- and underidentification in detail and provide easily verifiable conditions.
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PDF链接：
https://arxiv.org/pdf/1910.04096