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摘要暂缺

统计分析中的贝叶斯推断AI 解读

Nature of Bayesian Inference Standard Normal Theory Inference Problems Bayesian Assessment of Assumptions: Effect of Non-Normality on Inferences About a Population Mean with Generalizations Bayesian Assessment of Assumptions: Comparison of Variances Random Effect Models Analysis of Cross Classification Designs Inference About Means with Information from More than One Source: One-Way Classification and Block Designs Some Aspects of Multivariate Analysis Estimation of Common…

过度识别约束子集的检验AI 解读

A family of tests of significance is developed for coefficients in a single equation of a simultaneous system., Different members of this family are distinguished by the k-class estimator on which they are based, and on the alternative hypothesis against which they test. The size of the test is found when the disturbances are small, and the test is shown to be consistent if plim k = 1 or k = λ, the limited information maximum likelihood value.

摘要暂缺

IN THEIR ARTICLE [5], Liu and Breen propose a new estimator of the large-sample asymptotic covariance matrix for the limited information maximum likelihood estimator in simultaneous equations, and express surprise that their estimator is different from the estimator proposed by Chernoff and Divinsky [1]. Additionally, they question the interpretation of a statistic used in the past to test over-identifying restrictions.

扰动较小时 k 类估计量的比较AI 解读

A new approach to the choice of econometric estimators, called small-sigma asymptotics, is introduced and applied to the choice of k-class estimators of the parameters of a single equation in a system of linear simultaneous stochastic equations. I find that when the degree of overidentification is no more than six, the two stage least squares estimator uniformly dominates the limited information maximum likelihood estimator in a certain sense. The small sigma method can be…

小扰动情形下的过度识别约束检验AI 解读

Natural test statistics for the hypothesis that an equation is overidentified have been developed by Anderson and Rubin and by Basmann. If the disturbances are jointly normal, serially uncorrected, and small, both the above overidentification test statistics have the Fisher variance-ratio distribution asymptotically as the variance of the error terms gets small. This gives an analytic explanation of Monte Carlo results of Basmann. The results given apply to linear models in…