Refinements for Generalized Method of Moments Estimation and Testing

广义矩估计和测试方法的改进

• 批准号: 9409707
• 负责人: Whitney Newey
• 金额: $ 20.39万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-11-01 至 1997-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9409707&HistoricalAwards=false
• 关键词: Refinements; Generalized; Method; Moments; Estimation

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.

[409707]本项目为一些常用的计量经济学方法开发了改进的推理程序。目标是找到改进通常的大样本方法的近似方法，并且易于实现。该提案包括两个具体项目和扩展。项目包括：广义矩估计方法的自举和工具变量数量的选择。广义矩估计方法在计量经济学中应用广泛，因此需要可靠的推理方法。众所周知，通常的大样本推断在某些情况下并不奏效。本项目使用自举方法进行改进。改进将需要对众所周知的引导方法进行修改。它是基于从一个分布中采样，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。所提出的方法的有效性将通过经验和模拟实例来说明。提出的研究还将考虑将该方法扩展到其他模型，例如施加条件矩限制的模型。该项目还考虑了与工具变量的推断。工具变量估计是广义矩估计方法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“程序评估”模型估计的文献中特别有趣，其中工具变量被用来近似被处理的条件概率。本研究将使用渐近均方误差标准来推导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加以考虑。此外，本研究还将扩展到考虑其他模型中选择变量数量的规则，例如在计量经济学中广泛使用的样本选择模型。