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线性预测模型中聚合问题的计量分析AI 解读

This paper deals with the problem of aggregation where the focus of the analysis is whether to predict aggregate variables using macro or micro equations. The GrunfeldGriliches prediction criterion for choosing between aggregate and disaggregate equations is generalized to allow for contemporaneous covariances between the disturbances of micro equations and the possibility of different parametric restrictions on the equations of the disaggregate model. A new test is proposed…

用 Bahadur 方法比较分立参数族检验的渐近功效AI 解读

The paper investigates asymptotic power comparisons of tests of separate parametric families when the size of the test is allowed to tend to zero as the sample size tends to infinity. It shows that Bahadur's method of asymptotic comparison by means of the approximate slope of the tests can be extended to the case of separate parametric families. The paper contains a number of applications.

利用 Bahadur 方法比较不同参数族检验的渐近功效AI 解读

The paper investigates asymptotic power comparisons of tests of separate parametric families when the size of the test is allowed to tend to zero as the sample size tends to infinity. It shows that Bahadur's method of asymptotic comparison by means of the approximate slope of the tests can be extended to the case of separate parametric families. The paper contains a number of applications.

非嵌套回归模型不同检验的局部功效比较AI 解读

The paper derives and compares the local power of three different methods of testing non-nested regression models that are available in the literature. It shows that the asymptotic power of the orthodox F test against local alternatives is strictly less than that of Cox's non-nested test or the J test recently proposed by Davidson and MacKinnon [5], unless the number of non-overlapping variables of the alternative hypothesis over the null hypothesis is unity, in which case…

非嵌套非线性回归模型的检验AI 解读

In Pesaran [9], the test developed by Cox for comparing separate families of hypo-theses was applied to the choice between two non-nested linear single-equation econometric models. In this paper, the analysis is extended to cover multivariate nonlinear models whenever full information maximum likelihood estimation is possible. This allows formal comparisons not only of competing explanatory variables but also of alternative functional forms. The largest part of the paper…