Advances in Econometrics for Treatment Effect Bounds, Time-Varying-Parameter Nonstationary/Stationary Autoregressive Models, and Identification-Robust Inference

治疗效果界限、时变参数非平稳/平稳自回归模型和识别稳健推理的计量经济学进展

• 批准号: 1355504
• 负责人: Donald Andrews
• 金额: $ 25.81万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-15 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1355504&HistoricalAwards=false
• 关键词: Advances; Econometrics; Treatment; Effect; Bounds

This research project includes five different topics. The first develops new treatment effect bounds for the average treatment effect (ATE) in models with two continuous or discrete potential outcome variables, a binary treatment variable, and a binary instrumental variable (IV) that is independent of the potential outcomes. The ATE is not identified due to treatment effect heterogeneity. The research develops bounds on ATE that exploit the independence condition and an IV restriction by first considering the case where the treatment effect distribution is binary. The bounds hold for arbitrary treatment effect distributions. The bounds depend explicitly on the probability limit of the IV estimator and are of a simple form, which is conducive to inference. The assumptions imposed are neither stronger nor weaker than those currently considered in the literature. The results are applicable to treatment effect analysis in economics and to the analysis of medical randomized trials with incomplete compliance.The second portion develops deterministically time-varying autoregressive (AR) models that may exhibit (local) nonstationarity or stationarity and smooth transitions between the two. The PI considers estimation of the parameters by nonparametric smoothing in the time domain. Standard methods of reducing bias due to the time-varying parameters fail in the (locally) nonstationary case. Hence, new bias reduction methods will need to be introduced. Another important issue to be addressed is the endogenous character of the initial conditions for the local smoothing estimator, which are determined by the time-varying path of the sum of the AR coefficients. The PI analyzes methods for estimation, testing, CS construction, and forecasting. He also develops tests for the presence of time-varying parameters. This research will provide a useful new time series model that allows for time-varying nonstationarity/stationarity.Third, he develops inference methods that are robust to weak identification and identification failure in moment condition models. Several existing methods employ conditional likelihood ratio-type (CLR) tests and CS's that generalize the CLR test of Moreira (2003) for the linear IV regression model. Existing procedures (i) do not necessarily have correct asymptotic size when the dimension of the parameter is two or greater and (ii) do not reduce to Moreira?s CLR test in the linear IV model, which is known to have optimal power properties. The PI introduces new CLR-type procedures that do not have these deficiencies.The last two areas of research are on inference in partially-identified models that are defined by inequality restrictions on nonlinear functions of infinitely-many conditional moments and Lagrange multiplier tests under weak identification or lack of identification. This research develops new methods for the statistical analysis of social science data. The project will benefit society because it will improve the quality of data analysis used for a variety of important questions. These methods will be useful for economic policy analysis but will also be used by medical and engineering researchers who analyze data with similar statistical features.

该研究项目包括五个不同的主题。 第一个在具有两个连续或离散潜在结果变量、一个二元治疗变量和一个独立于潜在结果的二元工具变量（IV）的模型中为平均治疗效果（ATE）开发新的治疗效果界限。由于治疗效果异质性，ATE 未被识别。该研究通过首先考虑治疗效果分布是二元的情况，开发了利用独立条件和 IV 限制的 ATE 界限。 该界限适用于任意治疗效果分布。 该界限明确取决于 IV 估计量的概率极限，并且形式简单，有利于推理。所施加的假设既不比目前文献中考虑的假设强也不弱。 结果适用于经济学中的治疗效果分析以及不完全依从性的医学随机试验的分析。第二部分开发确定性时变自回归（AR）模型，该模型可能表现出（局部）非平稳性或平稳性以及两者之间的平滑过渡。 PI 考虑通过时域中的非参数平滑来估计参数。减少因时变参数引起的偏差的标准方法在（局部）非平稳情况下会失败。因此，需要引入新的偏差减少方法。另一个需要解决的重要问题是局部平滑估计器初始条件的内生特征，它由 AR 系数之和的时变路径决定。 PI 分析估计、测试、CS 构建和预测的方法。他还开发了针对时变参数是否存在的测试。这项研究将提供一种有用的新时间序列模型，允许时变非平稳性/平稳性。第三，他开发了对矩条件模型中的弱识别和识别失败具有鲁棒性的推理方法。几种现有方法采用条件似然比型 (CLR) 检验和 CS，将 Moreira (2003) 的 CLR 检验推广到线性 IV 回归模型。现有过程 (i) 当参数的维数为 2 或更大时，不一定具有正确的渐近大小，并且 (ii) 不会简化为线性 IV 模型中的 Moreira 的 CLR 测试，已知该模型具有最佳功效属性。 PI 引入了新的 CLR 型过程，没有这些缺陷。最后两个研究领域是部分识别模型的推理，这些模型由无限多个条件矩的非线性函数的不等式限制和弱条件下的拉格朗日乘数检验来定义识别或缺乏识别。这项研究开发了社会科学数据统计分析的新方法。 该项目将使社会受益，因为它将提高用于各种重要问题的数据分析的质量。 这些方法将有助于经济政策分析，但也将被分析具有类似统计特征的数据的医学和工程研究人员使用。