Robust Inference in Econometrics

这项研究旨在改进经济学和其他领域（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。标准统计方法的有效性依赖于某些基本假设。然而，这些假设并不能保证在实际应用中成立，它们的失败可能导致误导性的结果。本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，这些假设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鲁棒性，其中“识别”是指与感兴趣的对象相关的数据中可用的信息量。这项研究有可能使经济政策所依据的实证研究更加有力。识别鲁棒推理方面的文献在整体未知参数向量推理方面取得了长足的进展。本研究侧重于参数向量的线性和非线性函数，如子向量，它们在实践中通常比整个参数更有兴趣。研究者开发了对弱识别和识别失败完全稳健的方法（在一致意义上具有正确的渐近大小的意义上），不是渐近保守的，并且在强识别下是渐近有效的。这些方法将采用两步的方式，其中第一步涉及麻烦参数的置信集，第二步涉及使用数据依赖临界值的C（α）类型测试（或置信集）。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）在广泛使用的线性工具变量模型中测试的最优性特性，以及（iii）在具有两个内生变量的线性工具变量模型中，在弱识别和强识别下具有最优性特性的子向量测试。

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.