CAREER: Robust Inference in Incomplete Econometric Models

PROPOSAL NO：0348909 INSTITUTION：普林斯顿大学NSF项目：ECONOMICSPI：TAMER，ELILETITLE：CAREAR：稳健的不完全计量经济学模型本研究研究在具有最小似然假设的参数模型中出现的识别和估计问题。研究人员通常会强加事先的信息，有时甚至是不可检验的信息(假设)。如果这些假设是错误的，那么推断将具有误导性。本研究由四个项目组成，在不做强假设的情况下研究不完全模型中的推理。它将为实证研究人员提供更丰富的方法菜单，以使用更稳健的方法来解决给定的问题。这些稳健的方法揭示了各种常规的(点)辨识假设的效果。在没有进行特别假设的情况下，参数模型通常不会点辨识感兴趣的参数；相反，这些部分辨识的模型的辨识特征是一组稳健的参数值。这项研究表明，在部分识别的模型中进行稳健推理是可行的，并且与经验工作直接相关。第一个项目考察了在二元选择概率模型中忽略测量误差的效果。在没有进一步假设的情况下，在存在协变量测量误差的情况下，不能识别二元选择模型。这个项目将回答忽略测量误差是否会导致估计与“真相”“相去甚远”。这种推理方法可以用来在各种部分识别的模型中回答类似的问题。第二个项目提出了一种在经典面板数据问题中检验稳健性的方法。这类模型的一个问题是需要假设初始条件的分布以进行点识别。这项研究放宽了这些假设，提供了估计与计量经济学模型和数据一致的参数值的方法，该参数集对特定的和初始条件下不一致的假设是稳健的。第三个项目将提供离散K人博弈中推理的一般框架，并将其应用于研究航空业的市场结构。推理基于一类模型，该模型被定义为遵守必要的纳什均衡条件的一组效用函数。它将研究这类模型的识别特征，并将应用这些方法来检验一个重要的实证问题，从而展示这些模型的政策适用性。第四个项目描述了一种计量经济学方法，以获得以预先指定的概率在部分识别的模型中覆盖识别集的渐近有效的置信域。本研究通过本科生的研究经验，以及研究生助理的使用和稳健推理研究生课程的整合，将教学和研究结合在一起。

ABSTRACTPROPOSAL NO: 0348909INSTITUTION: Princeton UniversityNSF PROGRAM: ECONOMICSPI: Tamer, ElieTITLE: CAREER: Robust Inference in Incomplete Econometric ModelsThis research studies identification and estimation problems that arise in parametric models with minimal plausible assumptions. Researchers commonly impose prior and sometimes untestable information (assumptions). If these assumptions are wrong, inference will be misleading. This research consists of four projects to study inference in incomplete models without making strong assumptions. It will provide empirical researchers with a richer menu of approaches to tackle a given problem using more robust methods. These robust methods shed light on the effects of the various, routinely made (point) identification assumptions.Without making ad-hoc assumptions, oftentimes parametric models do not point identify the parameters of interest; rather, the identified feature of these partially identified models is a robust set of parameter values. This research shows that robust inference in partially identified models is practical, and of immediate relevance to empirical work. The first project examines the effect of ignoring measurement error in binary choice probit models. Without further assumptions, the binary choice model is not identified in the presence of covariate measurement error. This project will answer whether ignoring measurement error would lead to estimates that are "far away" from the "truth". This approach to inference can be used to answer similar questions in a wide variety of partially identified models.The second project proposes a way to examine robustness in a classic panel dataproblem. A problem in this class of models is the need to make assumptions distribution of initial condition for point identification. This research relaxes these assumptions by providing methods to estimate the set of parameter values that is consistent with the econometric model and the data, a parameter set that is robust to ad-hoc and inconsistent assumptions on the initial conditions. The third project will provide a general framework for inference in discrete K-player games and apply it to study market structure in the airline industry. Inference is based on a class of models that is defined as the set of utility functions that obey necessary Nash equilibrium conditions. It will study the identified feature of this class of models and will apply the methods to examine an important empirical problem, thus showing the policy applicability of these models. The fourth project describes an econometric methodology to obtain asymptotically valid confidence regions that cover the identified set in a partially identified model with a pre-specified probability. This research integrates teaching and research through research experience for undergraduates as well the use of graduate assistants and the integration of a graduate course on robust inference.