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 该项目开发了从经济数据中进行推断的改进方法。这些方法应该有助于在几个重要应用中对经济数据进行更准确的误差预测。该提案包括两个具体项目和扩展：（1）半参数模型中的高效引导：引导是一种重采样方法，可用于提高误差幅度预测的准确性。改进的程度取决于引导的完成方式。该项目开发了一种确定可用的最有效引导程序的理论。初步结果表明，有效的引导程序有很大的改进潜力。高效引导程序的新应用将用于进一步说明潜在的增益。 (2)选择矩条件的数量：矩估计量的广义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程序评估模型和动态增长模型。通常，力矩条件比可用参数多得多，了解如何在其中进行选择非常重要。这项研究将开发一种基于偏差和变异性之间权衡的方法。将研究该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型模拟研究中得到评估。