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Efficient Refinements for Large Sample Inference

大样本推理的高效改进

基本信息

批准号：
9810356
负责人：
Whitney Newey
金额：
$ 19.04万
依托单位：
Massachusetts Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
1998
资助国家：
美国
起止时间：
1998-08-01 至 2001-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=9810356&HistoricalAwards=false
关键词：
Efficient Refinements Large Sample Inference

项目摘要

9810356 Newey This project develops improved methods for drawing inferences from economic data. The methods should help make more accurate margin of error predictions for economic data in several important applications. The proposal includes two specific projects and extensions: (1) Efficient bootstrapping in semiparametric models: Bootstrapping is a resampling method that can be used to improve the accuracy of margin of error predictions. The degree of improvement can depend on how the bootstrapping is done. This project develops a theory for determining the most efficient bootstrap available. Preliminary results indicate that there is large potential for improvement from an efficient bootstrap. New applications of the efficient bootstrap will be used to further illustrate the potential gain. (2) Selecting the number of moment conditions: Generalized method of moment estimators have many applications in economics, including estimation of program evaluation models and dynamic models of growth using a time series of cross-sections. Often there are many more moment conditions than parameters available and it is important to know how to choose among them. This research will develop a method based on the tradeoff between bias and variability. The theoretical properties of this method will be studied and applications provided. Also, the practical value of this approach will be evaluated in some small simulation studies.

9810356 Newey这个项目开发了从经济数据中得出推论的改进方法。这些方法应该有助于在几个重要应用中对经济数据做出更准确的误差预测。该方案包括两个具体的项目和扩展：(1)半参数模型中的有效自举：自举是一种可用于提高误差预测精度的重采样方法。改进的程度可能取决于引导是如何完成的。该项目开发了一种确定最有效引导的理论。初步结果表明，一个有效的引导程序有很大的改进潜力。有效自举的新应用将被用来进一步说明潜在的增益。(2)矩条件个数的选择：广义矩估计方法在经济学中有许多应用，包括利用横截面时间序列估计方案评价模型和增长的动态模型。通常情况下，弯矩条件比可用的参数多得多，知道如何在它们中进行选择是很重要的。这项研究将开发一种基于偏差和变异性之间权衡的方法。该方法的理论性质将被研究并提供应用。此外，这种方法的实用价值将在一些小型模拟研究中进行评估。