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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.

小行星9810356 该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的应用中为经济数据做出更准确的误差预测。该建议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幅度预测精度的恢复方法。改进的程度可以取决于如何进行自举。这个项目开发了一个理论，用于确定最有效的引导可用。初步结果表明，有很大的潜力，从一个有效的引导改善。新的应用程序的有效引导将被用来进一步说明潜在的增益。 (2)选择矩条件的数量：广义矩估计方法在经济学中有许多应用，包括使用横截面的时间序列估计程序评估模型和动态增长模型。通常情况下，力矩条件比可用参数多得多，知道如何在其中进行选择很重要。本研究将开发一种基于偏倚和变异性之间权衡的方法。将研究这种方法的理论特性，并提供应用。此外，这种方法的实用价值将在一些小的模拟研究进行评估。