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结构方程中的 t 检验AI 解读

Properties of t ratios associated with the LIML, TSLS, and OLS estimators in a structural form estimation are studied. The existence of moments of these t ratios including the LIML form is proved first. Second, Monte Carlo simulations are performed to find out real sizes of the t test and the likelihood ratio test. Third, asymptotic expansions of the distributions of t ratios are derived under the null hypothesis to find out deviations of real sizes from nominal sizes…

回归系数非线性约束检验统计量零假设分布的高阶近似AI 解读

Asymptotic expansions of the distributions of likelihood ratio and Lagrange multiplier test statistics for nonlinear restrictions on regression coefficients are derived under the null hypothesis. Nonlinear restrictions include, as a special case, the identifiability restrictions in the simultaneous equations models. Our analyses of simultaneous equations deal not only with single equations but also subsystems and complete systems. The asymptotic expansions we derive are…

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联立方程系统中单方程估计量的比较AI 解读

Comparisons of estimators are made on the basis of their mean squared errors and their concentrations of probability computed by means of asymptotic expansions of their distributions when the disturbance variance tends to zero and alternatively when the sample size increases indefinitely. The estimators include k -class estimators (limited information maximum likelihood, two-stage least squares, and ordinary least squares) and linear combinations of them as well as…

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改进有限信息最大似然估计量分布的渐近展开AI 解读

An improvement of the limited information maximum likelihood (LIML) estimator was proposed by Morimune (1978). In this note two asymptotic expansions of the distribution of the improved estimator are derived, both for large sample size and for large values of the noncentrality parameter. The improved estimator uniformly dominates the LIML estimator in terms of the probability of concentration around the true coefficient for both the parameter sequences. The distributions of…

改进有限信息极大似然估计量分布的渐近展开AI 解读

An improvement of the limited information maximum likelihood (LIML) estimator was proposed by Morimune (1978). In this note two asymptotic expansions of the distribution of the improved estimator are derived, both for large sample size and for large values of the noncentrality parameter. The improved estimator uniformly dominates the LIML estimator in terms of the probability of concentration around the true coefficient for both the parameter sequences. The distributions of…

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不同参数序列下线性函数关系最大似然估计的改进AI 解读

We propose an improvement of the maximum likelihood (ML) estimate in linear functional relationships. The improved estimate is a linear combination of the ML and the least squares estimate so as to remove the bias of the former. Approximations to the distribution of the estimate are derived for two alternative parameter sequences: a sequence in which the noncentrality parameter (the spread of the true values) increases while the number of observations stays fixed, and that…

不同参数序列下线性函数关系最大似然估计的改进AI 解读

We propose an improvement of the maximum likelihood (ML) estimate in linear functional relationships. The improved estimate is a linear combination of the ML and the least squares estimate so as to remove the bias of the former. Approximations to the distribution of the estimate are derived for two alternative parameter sequences: a sequence in which the noncentrality parameter (the spread of the true values) increases while the number of observations stays fixed, and that…

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扰动较小时有限信息极大似然估计量的改进AI 解读

The limited information maximum likelihood (LIML) estimator is shown to be inadmissible in terms of asymptotic expansions of the distributions of the estimators. The LIML estimator is improved by combining it linearly with the two-stage or the ordinary least squares estimator.

小扰动情形下有限信息最大似然估计量的改进AI 解读

The limited information maximum likelihood (LIML) estimator is shown to be inadmissible in terms of asymptotic expansions of the distributions of the estimators. The LIML estimator is improved by combining it linearly with the two-stage or the ordinary least squares estimator.