Collaborative Research: Methods for Analyzing Large Dimensional Data

合作研究：大维数据分析方法

• 批准号: 0551275
• 负责人: Jushan Bai
• 金额: $ 13.33万
• 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: 2006
• 资助国家: 美国
• 起止时间: 2006-06-01 至 2009-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0551275&HistoricalAwards=false
• 关键词: Collaborative; Research; Methods; Analyzing; Large

Economists are fortunate to have access to lots of data, but the econometric tools that can beused to digest all the information remain rather limited. The standard assumption underlyingasymptotic analysis that treats N (number of cross-section units) as fixed and let T (the number oftime series observations) to tend to infinity is no longer appropriate for analyzing large data panels.The theme of the PIs research is efficient use of information in a large panel of data, say, X. The PI'swork will be organized around three projects. Project A continues the PIs previous work in using factor models to reduce the dimension of X. With N large, there is a need to carefully downweigh noisy data. The more difficult problem is to deal with the cross-section correlation in idiosyncratic errors that are not pervasive enough to be called common factors, but are strong enough to adversely affect the precision of the estimated common factors. In this grant, the PI's seek to develop moreefficient principal component estimators to deal with both problems.Project B continues to exploit the relevant information in X, but now the goal is to predictsome series, y, and the PI's step outside of the factor framework. The problem here is to pick out a setof reasonably strong predictors for y, but that the predictors are not very highly correlated witheach other, or else there will be too much information overlap. The PI's will use penalized regressions tostudy optimal shrinkage. The goal is to establish data dependent rules for the penalty parametersin a time series setting. For example, stationary and non-stationary predictors will be penalized atdifferent rates. Both in and out-of-sample predictions will be considered.Project C aims to develop an efficient estimator for panel cointegration in the presence of cross-section common shocks, which drive the comovement of economic variables. The framework allowsfor cross-sectionally correlated errors and encompasses the fixed effects model as a special case.Broader Impact and Intellectual Merit Standard principal component estimates are nowused in many forecasting exercises and in policy analysis. Improved factor estimates will inevitablyimpact these work. Project A should lead directly to better estimates for the number of factors,which has a natural role in asset pricing models and in demand analysis.In addition to providing results of immediate use to forecasters, Project B also impacts macroe-conomic analysis, as many economic models involve expectational variables. Economic hypothesescannot be fairly tested when the forecasts/conditional expectations are not properly modelled. Fur-thermore, instead of predicting y, a researcher might just want to predict if y is higher, lower, or stays thesame. The many predictors framework is potentially useful in broader contexts.When working with economic data, the assumption that the errors are iid across units is un-appealing. Project C tackles efficient estimation when the errors are cross-sectionally correlated.The results will be useful for economic analysis involving data for countries/industries/firms.

经济学家很幸运能够访问大量数据，但是可以使用来消化所有信息的计量经济学工具仍然相当有限。将n（横截面单元数）视为固定的标准假设分析，让t（时间串联观察的数量）倾向于无限属于无限，不再适合分析大型数据面板。PIS研究的主题是在大型数据集中有效地使用信息。项目A继续使用PIS先前的工作，以使用因子模型来降低X的尺寸。在n个较大的情况下，需要仔细地降低嘈杂的数据。更困难的问题是处理特质错误的横截面相关性，这些误差不足以被称为常见因素，但足够强大，可以不利地影响估计的共同因素的精度。在这笔赠款中，PI试图开发更有效的主成分估计器来处理这两个问题。项目B继续利用X中的相关信息，但现在的目标是预测闻名的系列，Y和PI在因子框架之外的台阶。这里的问题是为y选择一个合理强大的预测指标，但是预测变量与其他相关性并不高，否则信息会过多重叠。 PI将使用受惩罚的回归tostudy最佳收缩。目的是为罚款参数建立依赖数据的规则。例如，固定和非平稳预测因子将受到惩罚。将考虑在样本外预测中。该框架允许横截面相关的错误，并将固定效应模型作为特殊情况。Broader的影响和智力功绩标准主要成分估计现在已在许多预测练习和政策分析中使用。改进的因素估计将不可避免地影响这些工作。项目A应直接导致对因素数量的更好估计，该因素在资产定价模型和需求分析中具有自然作用。除了为预测者提供即时使用的结果外，项目B还影响了宏观工程学分析，因为许多经济模型都涉及预期变量。当预测/有条件期望未正确建模时，经济假设策划将进行公平测试。研究人员可能只想预测Y是更高，较低还是保持自己的状态，而不是预测Y。许多预测因素框架在更广泛的环境中可能有用。在使用经济数据时，假设错误是跨单位的IID的假设。当错误在横截面相关时，项目C对有效的估计进行了有效的估计。结果对于涉及国家/行业/公司的数据的经济分析将很有用。