Smoothly Mixing Regression Models

在应用科学研究和公共政策中，人们的兴趣往往集中在一些观察到的特征（例如，个人的年龄和教育）对特定事件（例如，个人的收入将超过贫困线）的概率的影响上。此外，这些影响可能会随着时间的推移而改变（例如，今天的大学毕业生可能比20世纪70年代的高中毕业生赚得更多）。该项目开发了使用现有数据库的定量方法，以可靠地评估观察到的特征对公共和私人决策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，导致对观察到的特征对感兴趣的结果的影响的评估比迄今为止更可靠和实用。该项目有三个主要组成部分。首先，它建立了新的结果，在经济计量学的近似条件下的条件分布，使用一系列的模型证明是实用和可靠的，在最近的研究调查，顺利混合回归模型的标题。其次，它解决了一系列的实际问题，以提高这些模型的效用，包括使用并行计算环境来处理大型数据集，在经济学中越来越常见，竞争模型的评估，并应用这些程序时，几个结果同时感兴趣。第三，它在两个主要背景下应用这些发展：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发展不需要限制性假设但能给出精确估计且计算可行的一般方法。该项目建立在许多过去的贡献，由调查员在这一主题，并应导致一个重大的进步，计算上可行的，但灵活的方法。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。该项目处理联邦统计系统中经常出现的问题。调查员迅速传播和应用研究成果，并通过在不同地点教授的为期一周的强化课程，定期与来自学术界、政府和私营部门的各种学生群体互动。

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.