Collaborative Research: Topics in Factor Analysis of Large Dimensions

合作研究：大维度因子分析主题

• 批准号: 0424540
• 负责人: Jushan Bai
• 金额: $ 14.82万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-06-01 至 2005-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0424540&HistoricalAwards=false
• 关键词: Collaborative; Research; Topics; Factor; Analysis

An inevitable outcome as we move forward in calendar time and as information technology advances is an increase in the volume of data available. Methods previously developed for handling a few variables are not adequate for analyzing hundreds of variables. This research is motivated by the need for empirical tools that can synthesize the data concisely and in ways that facilitate economic analysis. The focus of this research is on factor models. In a factor framework, components that have explanatory power in a large number of series are distinguished from the idiosyncratic ones that do not have pervasive effects on the data. This common-idiosyncratic decomposition provides an effective way of compressing a large volume of data to a manageablenumber of factors. Statistical results currently available for factor analysis assume either the time or the cross-section dimension of the panel is small. This research develops tools for factor analysis when both dimensions of the data panel are large. We show how the common and idiosyncratic components, although unobserved, can be consistently estimated from the data by the method of principal components whether or not the data are stationary. We will develop statistical criteria for determining the unknown number of factors from the data. We will also develop tests to determine whether non-stationarity in the observed data is of the common or idiosyncratic type. We will establish statistical properties of the principal components estimator. Different methods of estimating the common factors will also be considered.Many issues in macro and financial economics can be studied within a factor framework. For example, business cycles are characterized by the co-movement of a large number of economic time series. Asset returns have been shown to have a factor structure, with idiosyncratic variations being diversifiable, while systematic ones are not. Notions such as global trends and worldwide economic downturns are used frequently. The factor framework provides a formal treatment of these concepts. By providing the statistical foundation for factor analysis of large dimensions, our results enable researchers to use hundreds of series over decades, thousands of asset returns over years, and hundreds of country level series over centuries, to estimate the factors and conduct inference. The results of this research will make it possible to make maximum use of information available without having to choose subjectively which series are to be analyzed.

随着我们在日历时间前进和信息技术的进步，不可避免的结果是可用数据量的增加。以前开发用于处理一些变量的方法不足以分析数百个变量。这项研究是由对可以简化数据和促进经济分析方式综合数据的经验工具的必要性所激发的。 这项研究的重点是因素模型。在一个因素框架中，具有大量串联的解释能力的组件与对数据没有普遍影响的特质区分开。这种常见的iDiosyncratic分解提供了一种将大量数据压缩到管理因素的有效方法。 当前可用于因子分析的统计结果假设面板的时间或横截面维度很小。当数据面板的两个维度都大时，该研究开发了用于因素分析的工具。我们展示了如何通过主要成分方法从数据中始终从数据中始终估算的常见和特质组件，无论数据是否是静止的。我们将制定统计标准，以确定数据中未知数的因素。我们还将开发测试，以确定观察到的数据中的非平稳性是常见还是特殊类型。我们将建立主要成分估计器的统计属性。还将考虑估计共同因素的不同方法。可以在一个因素框架内研究宏观和金融经济学的许多问题。例如，商业周期的特征是大量经济时间序列的共同发展。资产回报已被证明具有因素结构，其特质变化是可多样化的，而系统的变化则不是。诸如全球趋势和全球经济衰退之类的概念经常使用。因子框架提供了对这些概念的正式处理。 通过为大小的统计基础提供统计基础，我们的结果使研究人员能够在数十年中使用数百个系列，数百年的资产回报和数百年来的数百个国家级级别，以估算因素和进行推断。这项研究的结果将使最大程度地利用可用的信息，而无需主观选择要分析哪个系列。