"Semiparametric Estimation and Inference in Partially Identified Econometric Models"

“部分确定的计量经济模型中的半参数估计和推理”

• 批准号: 1230071
• 负责人: Hiroaki Kaido
• 金额: $ 5万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1230071&HistoricalAwards=false
• 关键词: Semiparametric; Estimation; Inference; Partially; Identified

The econometrics literature has made substantial progress on estimation and inference methods for economic models, in which the parameter of interest is identified as a set. Yet, little is known about their properties when such partially identified models contain both finite and infinite dimensional parameters. Semiparametric models have been used widely in various empirical studies to make predictions and to conduct policy evaluations. They combine tractable parametric specification on key features of an economic model with flexible nonparametric restrictions on the rest. The main objective of this project is to expand the scope of semiparametric inference to major classes of partially identified econometric models. A particular focus will be placed on the theory of semiparametric efficiency. Recently, Kaido and Santos (2011) proposed an asymptotic efficiency concept for an important subset of partially identified models: the class of models defined by convex moment inequalities. The proposed research aims to expand the scope of this framework by studying other major classes of semiparametric partially identified models. Specifically two topics are considered.The first topic is on semiparametric regression models with an interval-censored variable. Interval censoring occurs frequently in micro-level data. The goal is to estimate a parameter that captures the marginal impacts of covariates on an interval-valued outcome variable without assuming any specific functional form of the regression function. The weighted average derivative of the regression function is one of such parameters. Although this parameter is not point identified in the presence of interval censoring, this approach may characterize its identified set. The researchers plan to study asymptotically efficient estimation of this set. The proposed efficient set-valued estimator will be useful for conducting empirical studies with survey data such as the Health and Retirement Study (HRS).The second topic studies efficient estimation of parameters indexed by a nuisance parameter. Many econometric models contain such parameters. Entry game models, for example, that are used to study various industries, contain structural parameters that could be fully recovered when the equilibrium selection rule were known. By varying the selection rule, the identified set can be equivalently viewed as a function of it. This project aims to to extend the efficiency concept developed in Kaido and Santos (2011) to study efficient estimation of this type of identified sets. Successful developments of inference methods for this class of models will be useful for conducting policy evaluations efficiently while allowing partial identification and flexible semiparametric specification.

计量经济学文献在经济模型的估计和推断方法方面取得了实质性的进展，其中感兴趣的参数被确定为一个集合。然而，很少有人知道他们的属性时，这种部分识别的模型包含有限和无限维参数。半参数模型已被广泛用于各种实证研究，以作出预测和进行政策评估。它们结合了联合收割机对经济模型关键特征的易处理的参数规范和对其余特征的灵活的非参数限制。这个项目的主要目标是扩大范围的半参数推断的主要类别的部分确定的计量经济模型。一个特别的重点将放在半参数效率的理论。最近，Kaido和桑托斯（2011）提出了部分可辨识模型的一个重要子集的渐近有效性概念：由凸矩不等式定义的一类模型。拟议的研究旨在扩大范围，通过研究其他主要类别的半参数部分识别模型的框架。具体地讲，本文讨论了两个问题：第一个问题是区间删失变量的半参数回归模型。区间删失经常发生在微观层面的数据。目标是估计一个参数，该参数捕获协变量对区间值结果变量的边际影响，而无需假设回归函数的任何特定函数形式。回归函数的加权平均导数是这样的参数之一。虽然该参数不是在区间删失存在下识别的点，但是该方法可以表征其识别集。研究人员计划研究这个集合的渐近有效估计。建议的有效集值估计将是有用的进行实证研究的调查数据，如健康和退休研究（HRS）。第二个主题研究的有效估计参数索引的滋扰参数。许多计量经济学模型都包含这样的参数。例如，用于研究不同行业的进入博弈模型包含的结构参数在已知均衡选择规则时可以完全恢复。通过改变选择规则，识别集可以等价地看作是它的函数。本项目旨在扩展Kaido和桑托斯（2011）提出的有效性概念，研究这类识别集的有效估计。这类模型的推理方法的成功发展将有助于有效地进行政策评估，同时允许部分识别和灵活的半参数规范。