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On Computer-Intensive Estimation and Testing Procedures in Econometrics

计量经济学中的计算机密集型估计和测试程序

基本信息

批准号：
14530033
负责人：
TANIZAKI Hisashi
金额：
$ 2.05万
依托单位：
Kobe University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2005
项目状态：
已结题

项目摘要

There are various kinds of nonparametric tests. We consider testing population mean, using the empirical likelihood ratio test. The empirical likelihood ratio test is useful in a large sample, but it has size distortion in a small sample. For size correction, various corrections have been considered. Here, we utilize the Bartlett correction and the bootstrap method. The purpose of this paper is to compare the $t$ test and the empirical likelihood ratio tests with respect to the sample power as well as the empirical size through Monte Carlo experiments.Moreover, we consider a nonparametric permutation test on the correlation coefficient. Because the permutation test is very computer-intensive, there are few studies on small-sample properties, although we have numerous studies on asymptotic properties with regard to various aspects. We aim to compare the permutation test with the $t$ test through Monte Carlo experiments, where an independence test between two samples is taken. We obtain the results through Monte Carlo experiments that the nonparametric test performs better than the $t$ test when the underlying sample is not Gaussian and that the nonparametric test is as good as the $t$ test even under the Gaussian population.In the case where the lagged dependent variables are included in the regression model, it is known that the ordinary least squares estimates (OLSE) are biased in small sample and that the bias increases as the number of the irrelevant variables increases. Based on the bootstrap methods, an attempt is made to obtain the unbiased estimates in autoregressive and non-Gaussian cases. We propose the residual-based bootstrap method. Some simulation studies are performed to examine whether the proposed estimation procedure works well or not. We obtain the results that it is possible to recover the true parameter values and that the proposed procedure gives us the less biased estimators than OLSE.

有各种各样的非参数检验。我们考虑使用经验似然比检验来检验总体均值。经验似然比检验在大样本情况下是有用的，但在小样本情况下存在尺寸失真。对于尺寸校正，已经考虑了各种校正。在这里，我们利用Bartlett校正和自助法。本文通过Monte Carlo实验比较了t检验和经验似然比检验在样本功效和经验规模方面的差异，并考虑了相关系数的非参数排列检验。由于置换检验是非常计算机密集型的，有很少的研究，小样本的性质，虽然我们有许多研究的渐近性质方面的各个方面。我们的目标是比较排列测试与$t$测试通过蒙特卡罗实验，其中两个样本之间的独立性测试。我们通过蒙特卡罗实验得到的结果是，当基础样本不是高斯样本时，非参数检验比$t$检验表现得更好，即使在高斯总体下，非参数检验也与$t$检验一样好。当滞后因变量包含在回归模型中时，普通最小二乘估计（OLSE）在小样本情况下是有偏的，并且随着无关变量个数的增加，其偏差也随之增大。基于Bootstrap方法，尝试获得自回归和非高斯情况下的无偏估计。我们提出了基于残差的bootstrap方法。一些模拟研究进行检查是否建议的估计程序工作良好或不。我们得到的结果，它是可能的，以恢复真实的参数值，所提出的程序给我们的估计比OLSE偏少。