Inference in Non-Standard Econometric Problems

非标准计量经济学问题的推理

• 批准号: 1226464
• 负责人: Ulrich Mueller
• 金额: $ 19.91万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1226464&HistoricalAwards=false
• 关键词: Inference; Non; Standard; Econometric; Problems

Empirical questions in economics are studied via econometric models. These models contain unknown parameters, which are typically the object of interest in empirical applications. For instance, one might be interested in learning about the average propensity to consume, or the marginal effect of a tax increase on labor supply, and researchers use data and econometrics analysis to estimate that parameter. Clearly, one would like to use methods of data analysis that allow one to learn as much as possible about these unknown parameters. And for a large set of models, it is well understood how to do so, at least approximately. With many observations from a well-behaved model, the problem of not knowing the parameter is effectively equivalent to the "standard problem" of observing a single Gaussian (bell-shaped) observation with known spread and a mean that corresponds to the unknown parameter. For a non-negligible subset of models, however, there is only an approximate equivalence to a different, "non-standard" problem. These unknown correlation patterns make it impossible to perfectly learn about the spread of the parameter estimator, which leads to a non-standard approximately equivalent problem where the spread of the single Gaussian vector observation is unknown. The proposal studies such non-standard problems, and seeks to develop ways of learning about the unknown parameters. The derivation of nearly efficient methods involves the development of appropriate analytical results, such accurate upper bounds on the potential quality of inference in non-standard problems, as well as numerical methods that determine a nearly efficient procedure, that is one with an inference quality close to the upper bound.

经济学中的实证问题是通过计量经济学模型来研究的。这些模型包含未知参数，这些参数通常是经验应用中感兴趣的对象。例如，人们可能有兴趣了解平均消费倾向，或者增税对劳动力供应的边际效应，研究人员使用数据和计量经济学分析来估计该参数。显然，人们希望使用数据分析方法来尽可能多地了解这些未知参数。对于大量模型来说，如何做到这一点是很好理解的，至少是大致如此。对于来自行为良好的模型的许多观测值，不知道参数的问题实际上等同于观察具有已知分布和对应于未知参数的均值的单个高斯（钟形）观测值的“标准问题”。然而，对于不可忽略的模型子集，仅存在与不同的“非标准”问题的近似等价。这些未知的相关模式使得不可能完美地了解参数估计器的扩展，从而导致单个高斯向量观测值的扩展未知的非标准近似等效问题。该提案研究了此类非标准问题，并寻求开发学习未知参数的方法。近高效方法的推导涉及适当的分析结果的开发，例如非标准问题中潜在推理质量的准确上限，以及确定近高效程序的数值方法，即推理质量接近上限的程序。