Identification and Inference in Some Econometrics Models

这一建议进一步深化了S研究员之前在计量经济模型中的识别和推断方面的工作，并涉及到一项详细的研究议程。这一议程的重点是在具有可信和容易解释的假设的参数模型中出现的识别和估计问题。这项建议由四个项目组成，研究经验相关模型中的识别和推理。该提案为实证研究人员提供了更丰富的方法菜单，以使用更稳健的方法来解决问题，从而在相关和重要的方向上推进了计量经济学文献。该提案中拟议的研究项目提供了一系列实用、有用和与实证工作直接相关的计量经济学模型。所提出的计量经济模型推进了对重要参数模型中已识别特征的识别和估计的研究。例如，在第一个项目中，将Heckman和Gronau的经典选择模型推广到具有多个决策者的多变量情况，其中参与阶段代表了一个完全信息博弈。例如，该模型与估计差异化产品需求的经验经济学家高度相关，因为它允许他们检查选择或内生产品质量对参数估计的影响。第二个项目计划提供在半参数似然模型中进行推理的方法，这些模型对点识别失败具有健壮性。这个项目与劳工和产业组织中使用的更广泛的模型相关。这种推理的敏感度分析方法很重要，可以用来回答各种部分识别的模型中的类似问题。第三个项目引入了中位数不相关的新概念，这与人们熟悉和经常使用的均值(线性)不相关的概念类似。然后，我们很容易地证明了这个概念导致了具有分位数限制的线性工具变量回归的透明估计。这是经验经济学中一个被广泛使用的领域。第四个项目通过提供用面板数据估计Roy类型选择模型的新方法，进一步推进了研究者在具有广义截尾形式的非线性模型中的推断工作。这类模型非常重要且与经验相关，并通过使用真实世界数据的实证例子说明了这种方法。提案的广泛影响：该提案中描述的研究提供了一个新的推理框架，并概述了可广泛使用的实用经验策略。它为应用经济学家和政策制定者提供了一套更丰富的工具，以进行强有力的推理和影响政策。这项建议还将计量经济学模型中的识别和推理研究议程整合到一个教育计划中，该计划不仅将直接使研究生受益，还将为优秀的本科生提供研究机会，并吸引计算经济学家和计算机科学家的参与和兴趣。

This proposal furthers the investigator?s previous work on identification and inference in econometric models and involves a detailed research agenda. The emphasis in this agenda is on the identification and estimation problems which arise in parametric models with plausible and easily interpretable assumptions. This proposal consists of four projects that study identification and inference in empirically relevant models. The proposal provides empirical researchers with a richer menu of approaches to tackle problems using more robust methods, and hence advances the econometric literature in relevant and important directions.The proposed research projects in this proposal provide an array of econometric models that are practical, useful, and of immediate relevance to empirical work. The proposed econometric models advance research on identification and estimation of the identified features in important classes of parametric models. For example, in the first project generalizes the classical selection model of Heckman and Gronau to the multivariate case with multiple decision makers where the participation stage represents a game of complete information. This model is highly relevant to empirical economists estimating differentiated product demand for example in that it allows them to examine the effect of selection, or endogenous product qualities, on parameter estimates. The second project plans on providing methods to conduct inference in semiparametric likelihood models that are robust to failure of point identification. This project is relevant to a wider class of models that are used in both labor and industrial organization. This sensitivity analysis approach to inference is important and can be used to answer similar questions in a wide variety of partially identified models. The third project introduces the novel concept of median uncorrelation that parallels the familiar and heavily used concept of mean (linear) uncorrelation. We then easily show that this concept leads to transparent estimators for linear instrumental variable regressions with quantile restrictions. This is a widely used area in empirical economics. The fourth project furthers the work of the investigator on inference in nonlinear models with generalized forms of censoring by providing new ways to estimate Roy type selection models with panel data. This class of models is very important and empirically relevant and illustrates this approach in an empirical example using real world data.Broader impacts of the proposal: The research described in this proposal provides a new framework for inference, and outlines practical empirical strategies that can be widely used. It provides applied economists and policy makers with a richer set of tools to conduct robust inference and influence policy. This proposal also integrates the research agenda on identification and inference in econometric models into an educational plan that will not only directly benefit graduate students, but will also provide research opportunities for outstanding undergraduates, and involve and interest computational economists and computer scientists.