Topics in Analysis of Big Data and Complex Models

拟议的研究旨在为研究人员提供新的方法来研究当前感兴趣的经济问题，有效利用最近才可用的大数据集。这些项目涉及数据预处理、估计和假设检验方面的问题。重点是具有广泛适用性的方法，可以投入实际使用。该研究也是多学科的，将统计学方法与计算机科学方法相结合，同时为任何领域的实证研究人员提供方法，这些研究人员使用统计方法处理大量数据集。第一个项目开发了高效和有效分析大数据的方法，以了解微观和宏观经济现象。虽然TB级的数据集越来越多，但资源限制往往使研究较小的观测集成为必要，这就提出了如何形成子样本的问题。研究人员将开发可以有效使用大型数据集的方法，同时保留对经济分析有价值的数据特征。第二个项目提供了频率论工具来评估估计结果对模型假设和数据特征的敏感性，这对于评估复杂结构模型的结果特别有用。第三个项目将评估不确定性是经济波动的原因还是后果。 鉴于没有理想的工具来区分不确定性冲击和真实的活动冲击，研究人员将开发一种迭代方法，从可能无效的工具中清除不必要的变化，以达到有效的工具。这种生成的外部IV程序通常可用于没有有效仪器可用的应用中。

The proposed research seeks to provide researchers with new methods to study economic issues of current interest, making effective use of big datasets that are only recently available. The projects deal with issues in data preprocessing, estimation, and hypothesis testing. The emphasis is on methods with broad applicability and that can be put to practical use. The proposed research is also multidisciplinary, combining methodologies from statistics with those from computer science, while providing methods for empirical researchers from any field that uses statistical methods on massive data sets.The proposed research consists of three projects. The first project develops methods for efficient and effective analysis of big data for the purpose of understanding micro and macroeconomic phenomenon. While datasets that are terabytes in size are increasingly available, resource constraints often make it necessary to study a smaller set of observations, which raises the question about how to form subsamples. The investigator will develop methods that can efficiently use large datasets, while preserving data features valuable to economic analysis. The second project provides frequentist tools to assess sensitivity of the estimation results to model assumptions and features of the data, which will be particularly useful to assess the results from complex structural models. The third project will assess whether uncertainty is a cause or a consequence of economic fluctuations. Given that there is no ideal instrument to distinguish uncertainty shocks from real activity shocks, the investigator will develop an iterative method that would purge the unwarranted variations from a potentially invalid instrument in order to arrive at a valid instrument. This generated external IV procedure can generally be used in applications when no valid instrument is available.