Identification and Inference in Some Econometrics Models

这一建议促进了调查？的识别和计量经济学模型的推理工作，并涉及详细的研究议程。这个议程的重点是识别和估计的问题，出现在参数模型与合理的和容易解释的假设。这个建议包括四个项目，研究识别和推理的经验相关的模型。该提案为实证研究者提供了一个更丰富的方法菜单，使用更强大的方法来解决问题，从而在相关和重要的方向推进计量经济学文献。该提案中建议的研究项目提供了一系列实用的计量经济学模型，这些模型与实证工作直接相关。所提出的计量经济学模型推进了对重要类别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，其中参与阶段代表完全信息的游戏。这个模型与实证经济学家估计差异化产品需求高度相关，例如，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方法，在半参数似然模型中进行推断，这种模型对点识别失败具有鲁棒性。这个项目是相关的更广泛的一类模型，用于劳动和产业组织。这种推理的敏感性分析方法很重要，可以用来回答各种各样的部分识别模型中的类似问题。第三个项目介绍了中位数不相关的新概念，它与熟悉和大量使用的平均（线性）不相关概念相似。然后，我们很容易地表明，这个概念导致透明的估计线性工具变量回归分位数的限制。这是实证经济学中广泛使用的领域。第四个项目通过提供新的方法来估计罗伊类型选择模型与面板数据，进一步研究了具有广义形式截尾的非线性模型中的推理。这类模型非常重要，并且与经验相关，并且在使用真实的世界数据的经验示例中说明了这种方法。提案的更广泛影响：本提案中描述的研究提供了一个新的推理框架，并概述了可以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来进行强有力的推理和影响政策。该提案还将计量经济学模型中的识别和推断的研究议程整合到教育计划中，这不仅将直接使研究生受益，而且还将为优秀的本科生提供研究机会，并使计算经济学家和计算机科学家参与并感兴趣。

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.