Testing Covariance Restrictions in Systems of Simultaneous Equations with Vector Autoregressive Errors
研究在误差服从向量自回归过程的线性联立方程系统中，如何检验关于误差协方差矩阵的零假设，给出了似然函数最大化的显式一阶条件，并推导了估计量的渐近方差协方差矩阵。
This paper considers inference procedures in a system of linear simultaneous equations with errors generated by a vector autoregressive process in situations where the null hypotheses involve the elements of the dispersion matrix of the errors. The problem is approached through the reduced form of the system and the first-order conditions for a maximum of the likelihood function are presented in an explicit form. This analysis in turn affords the development of the expressions for the asymptotic variance-covariance matrix of the estimated dispersion matrix, as well as the asymptotic covariance between these elements and the structural form parameter estimates under minimal assumptions on the actual distribution function of the errors. Copyright 1989 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.