A Note on Testing the Nested Structure in Multivariate Regression Models
提出一种在分析早期识别多元回归模型系数矩阵间嵌套结构的简单方法，基于联合正态估计量的Wald统计量渐近服从卡方分布，并通过协整分析中的数值例子和模拟研究验证其有效性。
In this article we propose a simple method of identifying, at an earlier stage of analysis, the nested structure among the coefficient matrices in multivariate regression models. When the limiting distribution of the estimators of the coefficient matrices are jointly normal, the Wald type statistics based on the proposed method is asymptotically a chi‐squared random variable. A numerical example that arises in cointegration analysis is provided to illustrate the method and a small simulation study is provided to illustrate its effectiveness.