Monte Carlo methodology and the finite sample properties of statistics for testing nested and non-nested hypotheses
Monte Carlo methodology and the finite sample properties of statistics for testing nested and non-nested hypotheses
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用于测试嵌套和非嵌套假设的蒙特卡罗方法和统计的有限样本属性
DOI：
10.17016/ifdp.1987.317
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发表时间：
1987
期刊：
影响因子：
--
通讯作者：
Neil R. Ericsson
中科院分区：
文献类型：
--
作者：
Neil R. Ericsson
利用最近发展起来的Monte Carlo方法，本文研究了动态性和随机性对最大似然和工具变量统计检验嵌套和非嵌套假设的有限样本性质的影响。数值分析近似（响应面）的未知的有限样本量和功率函数的这些统计动态的一个和两个方程模型。结果说明了渐近理论在解释有限样本性质和一定的局限性这样做的价值。两个实际的有限样本结果出现：F形式的沃尔德统计量是强烈的青睐，其卡方形式;和“大西格玛”的影响和一个小的有效样本量是特别明显的萨根（1958年）的工具变量统计和爱立信（1983年）的考克斯型工具变量统计。重新审视Pesaran和Deaton（1978）的经验例子，说明了从工具变量统计中获得的额外信息。
Using recently developed Monte Carlo methodology, this paper investigates the effect of dynamics and simultaneity on the finite sample properties of maximum likelihood and instrumental variables statistics for testing both nested and non-nested hypotheses. Numerical-analytical approximations (response surfaces) to the unknown finite sample size and power functions of those statistics are obtained for dynamic one-and two-equation models. The results illustrate the value of asymptotic theory in interpreting finite sample properties and certain limitations for doing so. Two practical finite sample results arise: the F form of the Wald statistic is strongly favored over its chi-squared form; and the effects of "large-sigma" and a small effective sample size are particularly pronounced for Sargan's (1958) instrumental variables statistic and Ericsson's (1983) Cox-type instrumental variables statistic. Re-examining Pesaran and Deaton's (1978) empirical example illustrates the additional information gained from the instrumental variables statistics.