Identification and Empirical Inference

识别与经验推论

• 批准号: 0314312
• 负责人: Charles Manski
• 金额: $ 26.11万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-01 至 2007-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0314312&HistoricalAwards=false
• 关键词: Identification; Empirical; Inference

Econometricians have long found it productive to study identification and statistical inference sequentially. One first analyzes identification of a population distribution and then considers induction from finite samples to the population, assuming that the finite sample properties are the same as that of the population. The PI has developed a major research program on partial identification of probability distributions. The research described here will extend this research program in two important directions. Policy makers make treatment choices based on a finite sample of data, knowing that different individuals should receive different treatments because of different responses to treatment. The first direction of extension is to use statistical decision theory to integrate the study of identification and statistical inference in the analysis of treatment response. The traditional way to cope with sampling processes that partially identify population parameters has been to combine the available data with strong assumptions to yield point identification. Such assumptions often are not well motivated, and empirical researchers often debate their validity. The approach proposed here allows researchers to learn from the available data without imposing untenable assumptions. Providing a framework for statistical treatment rules for treatment choices and parametric prediction with missing data, this research will help solve one of the major problems in econometrics and statistical inference. The second direction of extension is the computation of estimates of identification regions for parametric best predictors when data are missing. Many persistent public policy controversies reflect divergent beliefs about the effects of government policy on society. Such divergent beliefs are often manifested in competing policy studies that use different analytical approaches or data sources to reach different policy conclusions. However, there may be no way to determine which study (if either) makes realistic conjectures and which (if either) draws empirically correct conclusions. The research outlined here provides an innovative approach to empirical inference that enables the public to better evaluate the credibility of existing policy studies and can enhance the credibility of future policy research. The results of this research may not only have a strong impact on economic science, it is likely to have a strong impact on public policy formulation and evaluation.

计量经济学家长期以来发现按顺序研究识别和统计推断是富有成效的。 首先分析总体分布的识别，然后考虑从有限样本到总体的归纳，假设有限样本的属性与总体的属性相同。 PI 制定了一项关于概率分布的部分识别的重大研究计划。这里描述的研究将在两个重要的方向上扩展这个研究计划。 政策制定者根据有限的数据样本做出治疗选择，因为他们知道不同的个体由于对治疗的反应不同而应该接受不同的治疗。第一个扩展方向是利用统计决策理论将识别和统计推断的研究整合到治疗反应的分析中。 处理部分识别总体参数的抽样过程的传统方法是将可用数据与强有力的假设相结合以产生点识别。这些假设往往没有充分的动机，实证研究人员经常争论其有效性。这里提出的方法允许研究人员从现有数据中学习，而无需强加站不住脚的假设。这项研究为处理选择和缺失数据的参数预测提供了统计处理规则的框架，将有助于解决计量经济学和统计推断的主要问题之一。 扩展的第二个方向是当数据丢失时计算参数最佳预测器的识别区域的估计。 许多持续存在的公共政策争议反映了人们对政府政策对社会影响的不同看法。这种不同的信念通常体现在相互竞争的政策研究中，这些研究使用不同的分析方法或数据源来得出不同的政策结论。然而，可能无法确定哪项研究（如果有的话）做出了现实的推测，以及哪项（如果有的话）得出了经验上正确的结论。这里概述的研究提供了一种创新的实证推断方法，使公众能够更好地评估现有政策研究的可信度，并可以提高未来政策研究的可信度。 这项研究的结果不仅可能对经济科学产生重大影响，还可能对公共政策的制定和评估产生重大影响。