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Robust Inference in Econometrics

计量经济学中的稳健推论

基本信息

批准号：
1656313
负责人：
Donald Andrews
金额：
$ 22.61万
依托单位：
Yale University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1656313&HistoricalAwards=false
关键词：
Robust Inference Econometrics

项目摘要

This research is designed to improve the econometric and statistical methods available for empirical researchers in economics and other fields, including political science, medicine, and the physical sciences. Standard statistical methods rely on certain basic assumptions for their validity. However, these assumptions are not guaranteed to hold in practical applications, and their failure can lead to misleading results. This project contributes to the literature that aims to develop methods that rely on a much weak set of assumptions, which leads to more robust statistical procedures. Emphasis in the research is to make methods robust to weak identification and lack of identification, where "identification" refers to the amount of information available in the data concerning the object of interest. This research has the potential to make empirical research, upon which economic policy is based, more robust.The literature on identification-robust inference has made considerable progress on inference for an unknown parameter vector as a whole. This research focuses on linear and nonlinear functions of the parameter vector, such as subvectors, which typically are of much greater interest in practice than the whole parameter. The investigator develops methods that are completely robust to weak identification and identification failure (in the sense of having correct asymptotic size in a uniform sense), are not asymptotically conservative, and are asymptotically efficient under strong identification. The methods will be of a two-step fashion where the first-step involves a confidence set for the nuisance parameter and the second step involves a C(&#945;)-type test (or confidence set) that employs a data-dependent critical value. The investigator also carries out research on (i) deterministically time-varying autoregressive models that may exhibit (local) nonstationarity or stationarity and smooth transitions between the two, (ii) optimality properties of tests in the widely-used linear instrumental variables model, and (iii) subvector tests with optimality properties under weak and strong identification in the linear instrumental variable model with two endogenous variables.

本研究旨在改善经济学和其他领域（包括政治学，医学和物理科学）的实证研究人员可用的计量经济学和统计方法。标准的统计方法依赖于某些基本假设的有效性。然而，这些假设并不能保证在实际应用中成立，它们的失败可能会导致误导性的结果。该项目有助于文献，旨在开发依赖于一组弱得多的假设，从而导致更强大的统计程序的方法。研究的重点是使方法对弱识别和缺乏识别具有鲁棒性，其中“识别”是指有关感兴趣对象的数据中可用的信息量。这一研究有可能使经济政策所依据的实证研究更加稳健。辨识稳健推断的文献在未知参数向量的整体推断方面取得了相当大的进展。本研究的重点是线性和非线性函数的参数向量，如子向量，这通常是更大的兴趣，在实践中比整个参数。研究人员开发的方法是完全强大的弱识别和识别失败（在这个意义上有正确的渐近大小在统一的意义上），是不是渐近保守的，是渐近有效的强识别。该方法将是一个两步的方式，其中第一步涉及的滋扰参数的置信度集和第二步涉及的C（#945;）-类型的测试（或置信度集），采用数据依赖的临界值。研究者还对（i）确定性时变自回归模型进行了研究，这些模型可能表现出（局部）非平稳性或平稳性以及两者之间的平滑过渡，（ii）广泛使用的线性工具变量模型中检验的最优性，以及（iii）在具有两个内生变量的线性工具变量模型中弱识别和强识别下具有最优性的子向量检验。