Issues in Estimation of Structural Economic Models

这个项目主要是方法论的，主要贡献将是提供工具和见解，这将对经济学(和相关领域)的许多领域的研究人员有用。重点将放在统计模型的估计上，这些模型旨在纳入和检验经济理论所隐含的限制。应用研究人员在估计这类模型时面临许多问题。该项目将专注于其中的两个。第一个问题是，从这些模型中产生令人信服的统计证据可能非常耗时。该项目将引入和探索一种新的统计方法，这将极大地减少这一计算负担。这个项目的第二个问题是，即使在原则上，在没有武断和不可检验的假设的情况下，也能在多大程度上估计一些标准的经济模型。该项目的研究结果将对应用经济学的许多领域的研究人员有帮助，包括劳动经济学、公共财政、产业组织和宏观经济学，从而促进科学的发展。由于这些经济模型和统计方法现在被广泛用于衡量商业方法和政府政策的效果，这项研究也有助于国家的繁荣。最重要的新计量经济学工具是一种类似自举的方法。该工具背后的主要思想是将通常的Bootstrap方法应用于许多一维估计器。该项目将利用这样一个事实，即所得到的联合分布包含估计与感兴趣的参数向量相关联的标准误差所需的所有信息。这种类似Bootstrap的方法将极大地降低与估计复杂模型(如结构计量经济学模型)相关的计算负担。该项目的第二个智力贡献将是提供一组关于动态罗伊模型的新结果，这将有助于理解人们可以和不能从这些模型中的观测数据中估计什么。该项目还将对独立假设进行新的统计测试，这是研究人员在估计持续期模型时通常做出的假设。最后，该项目将评估一个动态离散选择模型，该模型旨在揭示家庭退休决策。实证研究的焦点将是配偶一方S的退休时间对另一方退休时间的影响程度。

This project is mostly methodological, and the main contribution will be to provide tools and insights that will be of use for researchers in many areas of economics (and related fields). The focus will be on the estimation of statistical models, which are designed to incorporate and test restrictions implied by economic theory. Applied researchers face many issues when estimating such models. The project will focus on two of them. The first issue is that it can be very time-consuming to produce convincing statistical evidence from these models. The project will introduce and explore a new statistical method, which will dramatically reduce this computational burden. The second issue of this project regards the extent to which it is, even in principle, possible to estimate some standard economic models without arbitrary and untestable assumptions. The findings of this project will be useful for researchers in many areas of applied economics, including labor economics, public finance, industrial organization and macroeconomics, and therefore advances science. Because these economic models and statistical methods are now widely used to measure the effects of business methods and government policies, the research also contributes to the national prosperity. The most important new econometric tool is a bootstrap-like method. The main idea behind the tool is to apply the usual bootstrap method to a number of one-dimensional estimators. The project will exploit the fact that the resulting joint distribution contains all the information that one needs to estimate the standard errors associated with the parameter-vector of interest. This bootstrap-like method will dramatically lower the computational burden associated with estimating complicated models such as structural econometric models. The second intellectual contribution of the project will be to provide a set of new results about Dynamic Roy Models, which will be useful for understanding what one can and cannot hope to estimate from observational data in these models. The project will also deliver a new statistical test of an independence assumption, which is commonly made by researchers estimating duration models. Finally, the project will estimate a dynamic discrete choice model designed to shed light on household retirement decisions. The empirical focus will be on the extent to which one spouse?s retirement time affects the retirement timing of the other.