Econometric Methods for Exploiting New Data in Macroeconomics

新的丰富数据集，包括个别公司和家庭数据，已经可以帮助中央银行和研究人员更好地监测和分析整体经济。这些数据集使研究人员能够确定政策变化的因果影响。 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力量和透明度;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体如何与单个企业和家庭相互作用，以及这些关系如何随着时间的推移而变化。 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。 本研究提案包括三个项目，以开发分析新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，预测方差分解和历史分解的部分或点识别的条件下比现有方法所需的“可逆性”假设弱。第二个项目考虑结构模型中的力矩匹配推理。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，它仍然可以执行有效的推理和计算的时刻的最佳加权。第三个项目考虑使用微观和宏观数据的异构代理模型的全信息估计。该项目设计了一个全面有效的贝叶斯推理的存在下，未观察到的聚集状态，影响横截面分布的一般方法。 这项研究的结果将改善经济政策的制定，从而提高经济增长和美国人的生活水平。这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。

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.