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Identification and Inference in Structural Models

结构模型中的识别和推理

基本信息

批准号：
0136869
负责人：
Whitney Newey
金额：
$ 25.68万
依托单位：
Massachusetts Institute of Technology
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2002
资助国家：
美国
起止时间：
2002-07-01 至 2006-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0136869&HistoricalAwards=false
关键词：
Identification Inference Structural Models

项目摘要

Structural estimation is vital in empirical economics. The need for structural estimation arises from confounding factors due to individual choice making and market forces. A classical application is estimation of the effect of tax changes in a particular market, where structural estimation can be used to separate supply and demand factors. This research will study how to do structural estimation without functional form assumptions, which will help avoid misleading inferences that do occur in applications. A menu of models and methods will be developed. The research will also develop statistical tools to help in selecting among the methods. These tools will evaluate the performance of the different methods using higher-order approximations, and help determine which method is best. Previous work shows that avoiding functional form assumptions in structural modeling can lead to more accurate inferences in important applications such as evaluating the effect of tax changes on labor supply or consumer well being. This research will add significantly to our ability to do structural estimation by developing methods for some of the most important models. Also, the study will show that certain of the methods have particularly good statistical properties. This research will show that among methods that are currently widely used for estimating models in both microeconomics and macroeconomics the one called empirical likelihood has particularly attractive properties. These results suggest that using this particular method may lead to more accurate inference in empirical research.

结构估计在实证经济学中至关重要。由于个人选择和市场力量的混杂因素，需要进行结构性估计。一个经典的应用是估计税收变化对特定市场的影响，其中结构性估计可用于分离供给和需求因素。本研究将研究如何在没有函数形式假设的情况下进行结构估计，这将有助于避免在应用中确实发生的误导性推断。将制定一系列模型和方法。这项研究还将开发统计工具，以帮助在各种方法中进行选择。这些工具将使用高阶近似来评估不同方法的性能，并帮助确定哪种方法是最好的。以前的工作表明，在结构建模中避免函数形式假设可以在重要应用中导致更准确的推断，例如评估税收变化对劳动力供应或消费者福祉的影响。这项研究将通过为一些最重要的模型开发方法来显着增加我们进行结构估计的能力。此外，该研究将表明，某些方法具有特别好的统计特性。这项研究将表明，在目前广泛用于估计微观经济学和宏观经济学模型的方法中，称为经验似然的方法具有特别吸引人的特性。这些结果表明，使用这种特殊的方法可能会导致更准确的推理，在实证研究。