Refinements for Generalized Method of Moments Estimation and Testing

9409707 Newey这个项目为一些常用的经济计量方法开发了改进的推理程序。目标是找到在通常的大样本方法的基础上改进的、易于实现的近似方法。该提案包括两个具体项目和延期。这些项目包括：广义矩估计方法的自举和工具变量个数的选择。广义矩估计方法在计量经济学中得到了广泛的应用，因此需要可靠的推断方法。众所周知，通常的大样本推断在某些情况下不能很好地发挥作用。这个项目使用Bootstrap方法进行了改进。改进将需要对众所周知的引导方法进行修改。它基于分布的抽样，该分布施加了与估计器相同的矩限制，这与通常的自举不同。通过实验和仿真算例说明了所提方法的有效性。拟议的研究还将考虑将该方法扩展到其他模型，例如那些施加条件矩限制的模型。该项目还考虑了工具变量的推理。工具变量估计量是广义矩估计量中应用最广泛的一种。一个重要的实际问题是选择在特定应用中使用的工具变量的数量。这个问题在最近关于“方案评估”模型估计的文献中特别感兴趣，其中工具变量被用来近似被治疗的条件概率。这项研究将使用渐近均方误差标准来推导出一些选择工具变量数量的简单规则。选择规则的有效性将在经验和模拟实例中考虑。此外，这项研究还将扩展到考虑其他模型中变量数量的选择规则，例如计量经济学中广泛使用的样本选择模型。

9409707 Newey This project develops improved inference procedures for some frequently used econometric methods. The goal is to find approximations that improve on the usual large sample approach, and that are easy to implement. The proposal includes two specific projects and extensions. The projects are: bootstrapping for generalized methods of moments estimation and selecting the number of instrumental variables. Generalized method of moments estimation is widely applied in econometrics, so that reliable inference methods are needed. It is known that the usual large sample inferences do not work well in some cases. This project develops an improvement using bootstrap methods. The improvement will require a modification of well known bootstrap methods. It is based on sampling from a distribution that imposes the same moment restrictions as the estimator, which is different than the usual bootstrap. The usefulness of the proposed methods will be illustrated by empirical and simulation examples. The proposed research will also consider extensions of the approach to other models such as those where conditional moment restrictions are imposed. The project also considers inference with instrumental variables. Instrumental variables estimators are one of the most widely applied types of generalized method of moments estimators. An important practical problem is the choice of the number of instrumental variables to use in particular applications. The problem is of particular interest in the recent literature on estimation of "program evaluation" models, where instrumental variables are used to approximate the conditional probability of being treated. This research will use an asymptotic mean-square error criteria to derive some simple rules for choosing the number of instrumental variables. The efficacy of the selection rule will be considered in empirical and simulation examples. Also this research will be extended to consider rules for choosing the number of variables in other models, such as the sample selection model that has been widely used in econometrics.