CAREER: Robust Inference in Incomplete Econometric Models

项目编号：0348909机构：普林斯顿大学nsf项目：经济学spi: Tamer， ElieTITLE：职业：不完全计量经济模型中的稳健推断本研究研究了在参数模型中出现的具有最小似是而非假设的识别和估计问题。研究人员通常会强加先验的，有时是不可检验的信息（假设）。如果这些假设是错误的，推论就会产生误导。本研究包括四个项目来研究不完全模型在不做强假设的情况下的推理。它将为实证研究人员提供更丰富的方法菜单，以使用更可靠的方法来解决给定的问题。这些可靠的方法阐明了各种常规的（点）识别假设的影响。如果不做特别的假设，参数化模型往往不能指出感兴趣的参数；相反，这些部分识别模型的识别特征是一组鲁棒的参数值。这项研究表明，在部分确定的模型中稳健的推断是实用的，并且与实证工作直接相关。第一个项目检验忽略测量误差在二元选择概率模型中的影响。如果没有进一步的假设，在协变量测量误差的存在下，二元选择模型不能被识别。这个项目将回答忽略测量误差是否会导致估计“远离”“真相”。这种推理方法可用于回答各种部分识别模型中的类似问题。第二个项目提出了一种检验经典面板数据问题稳健性的方法。这类模型的一个问题是需要对点识别的初始条件进行假设分布。本研究通过提供与计量经济模型和数据一致的参数值集的估计方法来放宽这些假设，该参数集对初始条件下的临时和不一致假设具有鲁棒性。第三个项目将提供离散k玩家博弈的一般推理框架，并将其应用于航空业的市场结构研究。推理是基于一类被定义为服从必要纳什均衡条件的效用函数集的模型。它将研究这类模型的识别特征，并将应用这些方法来检查一个重要的经验问题，从而显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，以预先指定的概率获得覆盖部分识别模型中识别集的渐近有效置信区域。本研究通过对本科生的研究经验，以及使用研究生助理和整合研究生课程的鲁棒推理，将教学和研究结合起来。

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.