Econometric Methods for Exploiting New Data in Macroeconomics

包括单个企业和家庭数据在内的新的丰富数据集已经可用，以帮助央行和研究人员更好地监测和分析整个经济。这些数据集使研究人员能够确定政策变化的因果关系。这些新的数据来源为商业周期和经济政策的分析带来了更大的权力和透明度；然而，现有的计量经济学方法不允许研究人员充分利用这些改进的数据集来全面分析总体经济。该研究项目将开发新的计量经济学方法，使研究人员和中央银行能够充分利用这些新类型的数据来解释更大的经济如何与单个企业和家庭相互作用，以及这些关系如何随时间变化。这项研究的结果将改善经济政策的制定和对国民经济的监测，从而促进经济增长，提高美国人的生活水平。本研究计划包括三个项目，以发展分析新的宏观经济数据的方法。第一个项目考虑使用工具变量对不同经济冲击的重要性进行半结构性识别。该项目表明，与现有方法所要求的“可逆性”假设相比，预测方差分解和历史分解在较弱的条件下是部分或点识别的。第二个项目考虑结构模型中的矩匹配推理。研究人员知道匹配矩的方差，而来自不同来源的矩的相关结构往往是未知的。该项目表明，仍然有可能执行有效的推理并计算力矩的最佳加权。第三个项目考虑了使用微观和宏观数据的异构智能体模型的全信息估计。该项目设计了一种通用方法，用于在影响横截面分布的未观察到的聚合状态存在时进行完全有效的贝叶斯推断。这项研究的结果将改善经济政策制定，从而提高经济增长和美国人的生活水平。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

New rich data sets that include individual firm and household data have become available to help central banks and researchers better monitor and analyze the total economy. These data sets allow researchers to establish causal effects of policy changes. These new data sources have brought increased power and transparency to the analyses of business cycles and economic policy; however, existing econometric methods do not allow researchers to take full advantage of these improved data sets to fully analyze the aggregate economy. This research project will develop new econometric methods that will allow researchers and central banks to fully use these new types of data to explain how the larger economy interacts with individual firms and households and how these relationships change over time. The results of this research will improve economic policy making and monitoring of the national economy and thus increase economic growth and improve the livings standards of Americans. This research proposal consists of three projects to develop methods for analyzing new macroeconomic data. The first project considers semi-structural identification of the importance of different economic shocks using instrumental variables. The project shows that forecast variance decompositions and historical decompositions are partially- or point-identified under weaker conditions than the "invertibility" assumption required by existing methods. The second project considers moment matching inference in structural models. Researchers know the variances of matched moments, whereas the correlation structure of moments arising from disparate sources is often unknown. The project demonstrates that it is still possible to perform valid inference and compute an optimal weighting of the moments. The third project considers full-information estimation of heterogeneous agent models using both micro and macro data. The project devises a general method for a fully efficient Bayesian inference in the presence of unobserved aggregate states that affect cross-sectional distributions. The results of this research will improve economic policy making and thus increase economic growth and livings standards of Americans.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.