Robust Inference in Econometrics

这项研究旨在改进经济学和其他领域的实证研究人员可用的计量经济学和统计学方法，包括政治学、医学和物理学。标准统计方法的有效性依赖于某些基本假设。然而，这些假设在实际应用中并不一定成立，它们的失败可能会导致误导性的结果。这个项目对旨在开发依赖于一组非常弱的假设的方法的文献做出了贡献，这导致了更稳健的统计程序。研究的重点是使方法对弱识别和缺乏识别具有健壮性，其中“识别”是指关于感兴趣对象的数据中可获得的信息量。这项研究有可能使经济政策所依据的实证研究更具稳健性。关于识别-稳健推理的文献在对未知参数向量的整体推断方面取得了相当大的进展。这项研究集中于参数向量的线性和非线性函数，例如子向量，它们在实践中通常比整个参数更感兴趣。研究者提出的方法对弱辨识和辨识失败(在一致意义下具有正确的渐近大小)是完全鲁棒的，不是渐近保守的，并且在强辨识下是渐近有效的。这些方法将分两步进行，其中第一步涉及滋扰参数的置信度集，第二步涉及采用依赖于数据的临界值的C(&amp；#945；)类型测试(或置信集)。(I)确定性时变自回归模型可能表现出(局部)非平稳性或平稳性以及两者之间的平稳过渡；(Ii)广泛使用的线性辅助变量模型检验的最优性；(Iii)具有两个内生变量的线性辅助变量模型在弱识别和强识别下具有最优性的子向量检验。

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(α)-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.