Smoothly Mixing Regression Models

平滑混合回归模型

• 批准号: 0720547
• 负责人: John Geweke
• 金额: $ 20.99万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0720547&HistoricalAwards=false
• 关键词: Smoothly; Mixing; Regression; Models

In applied scientific research as well as in public policy, interest often centers on the influence of some observed characteristics (for example, an individual's age and education) on the probability of a particular event (for example, that an individual's earnings will exceed the poverty threshold). Moreover, these influences may change over time (for example, college graduates may earn more compared to high school graduates today than they did in the 1970's). This project develops quantitative methods for using existing databases to reliably assess the impact of observed characteristics on events that are important in public and private policymaking as well as applied scientific research. The project extends recent advances in the fields of econometrics, statistics and computer science leading to assessments of the influence of observed characteristics on outcomes of interest that are more reliable and practical than has heretofore been possible. The project has three main components. First, it establishes new results in econometrics on the approximation of conditional distributions under weak conditions, using a sequence of models demonstrated to be practical and reliable in the recent research of the investigator, the smoothly mixing regression models of the title. Second, it addresses a series of practical issues in order to enhance the utility of these models, including the use of parallel computing environments to handle large data sets that are increasingly common in economics, the evaluation of competing models, and application of these procedures when several outcomes are simultaneously of interest. Third, it applies these developments in two leading contexts: the aforementioned example involving the influence of individual characteristics on earnings, using the Current Population Survey of the U.S. Census, and measuring the risk associated with investment in financial assets. Broader impacts: One of the major themes in econometrics and statistics is the development of general methods that do not require restrictive assumptions yet give precise estimates and are computationally feasible. This project builds on the many past contributions by the investigator on this theme and should lead to a major advance in computationally feasible yet flexible methods. These methods will benefit research in a wide range of disciplines including economics, statistics, public health, biostatistics and environmental sciences. The project addresses questions that arise regularly in the Federal statistical system. The investigator rapidly disseminates and applies research findings and regularly interacts with a diverse group of students from academia, government and the private sector through intensive one-week courses taught in various locations.

在应用科学研究和公共政策中，人们的兴趣通常集中在一些观察到的特征（例如，个人的年龄和教育程度）对特定事件的概率（例如，个人的收入将超过贫困线）的影响。此外，这些影响可能会随着时间的推移而发生变化（例如，与 1970 年代相比，当今大学毕业生的收入可能比高中毕业生高）。该项目开发定量方法，使用现有数据库可靠地评估观察到的特征对公共和私人政策制定以及应用科学研究中重要事件的影响。该项目扩展了计量经济学、统计学和计算机科学领域的最新进展，从而对观察到的特征对感兴趣的结果的影响进行评估，这种评估比以往任何时候都更加可靠和实用。该项目由三个主要部分组成。首先，它使用了研究者最近研究中证明实用且可靠的一系列模型，即标题的平滑混合回归模型，建立了计量经济学中关于弱条件下条件分布近似的新结果。其次，它解决了一系列实际问题，以增强这些模型的实用性，包括使用并行计算环境来处理经济学中日益常见的大型数据集、竞争模型的评估以及当多个结果同时感兴趣时这些程序的应用。第三，它将这些发展应用于两个主要背景：前面提到的例子涉及个人特征对收入的影响，使用美国人口普查的当前人口调查，以及衡量与金融资产投资相关的风险。更广泛的影响：计量经济学和统计学的主要主题之一是开发不需要限制性假设但可以给出精确估计并且在计算上可行的通用方法。该项目建立在研究者过去就该主题做出的许多贡献的基础上，应该会导致计算上可行且灵活的方法取得重大进展。这些方法将有利于经济学、统计学、公共卫生、生物统计学和环境科学等广泛学科的研究。该项目解决了联邦统计系统中经常出现的问题。研究人员迅速传播和应用研究成果，并通过在不同地点教授为期一周的强化课程，定期与来自学术界、政府和私营部门的不同学生群体进行互动。