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结构方程的归一化：方向估计量的性质AI 解读

In the general structural equation model only the direction of the vector of coefficients of the endogenous variables is determined.The traditional normalization rule defines the coefficients that are of interest but should not be embodied in the estimation procedure: we show that the properties of the traditionally defined ordinary least squares and two stage least squares estimators are distorted by their dependence on the normalization rule.Symmetrically normalized…

若干计量检验问题的相似区域类别及其功效性质AI 解读

In an hypothesis testing problem involving nuisance parameters for which boundedly complete sufficient statistics exist under the null hypothesis, the class of all similar regions for the problem is characterized by the conditional distribution of the data given these sufficient statistics. If there exists a one-to-one transformation y → ( t, u ) of the data, y , to the sufficient statistic, t , and a second vector of statistics, u , that is independent of t under the null…

非负回归系数零约束的联合检验AI 解读

Three tests for zero restrictions on regression coefficients that are known to be nonnegative are considered: the classical F test, the likelihood ratio test, and a one-sided t test a particular direction. Critical values for the likelihood ratio test are given for the cases of two and three restrictions, and the power function is calculated for the case of two restrictions. The analysis is conducted in terms of a characterization of the clas all similar tests for the…

摘要暂缺

非负回归系数零约束的联合检验AI 解读

Three tests for zero restrictions on regression coefficients that are known to be nonnegative\nare considered: the classical F test, the likelihood ratio test, and a one-sided t test\nin a particular direction. Critical values for the likelihood ratio test are given for the\ncases of two and three restrictions, and the power function is calculated for the case of\ntwo restrictions. The analysis is conducted in terms of a characterization of the clas\nall similar tests for…

线性回归误差协方差矩阵的局部最优不变检验AI 解读

SUMMARY This paper considers a class of hypothesis testing problems concerning the covariance matrix of the disturbances in the classical linear regression model. A test that is locally best invariant against one-sided alternative hypotheses is constructed and shown to be identical to a one-sided version of the Lagrange Multiplier test.

一般结构方程中工具变量估计量的联合与边际密度AI 解读

Starting from the conditional density of the instrumental variable (IV) estimator given the right-hand-side endogenous variables, we provide an alternative derivation of Phillips' result on the joint density of the IV estimator for the endogenous coefficients, and derive an expression for the marginal density of a linear combination of these coefficients. In addition, we extend Phillips' approximation to the joint density to 0( T −2 ,) and show how this result can be used to…