摘要翻译：
介绍了一种高斯混合向量自回归模型的结构形式。将误差项协方差矩阵的同时对角化与零约束和符号约束相结合，识别激波。事实证明，这通常导致比传统的SVAR模型更少的限制性识别条件，同时一些约束也是可测试的。随附的R-package gmvarkit为估计模型和应用所介绍的方法提供了易于使用的工具。
---
英文标题：
《Structural Gaussian mixture vector autoregressive model》
---
作者：
Savi Virolainen
---
最新提交年份：
2020
---
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。
--
一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
--

---
英文摘要：
  A structural version of the Gaussian mixture vector autoregressive model is introduced. The shocks are identified by combining simultaneous diagonalization of the error term covariance matrices with zero and sign constraints. It turns out that this often leads to less restrictive identification conditions than in conventional SVAR models, while some of the constraints are also testable. The accompanying R-package gmvarkit provides easy-to-use tools for estimating the models and applying the introduced methods.
---
PDF链接：
https://arxiv.org/pdf/2007.04713