Macreconomics as Explicitly Aggregated Microeconomics

21世纪世纪经济学最大的变化之一是关于家庭和企业的全面、高质量、微观层面的数据越来越多。为了有意义地利用所有这些数据，我们需要具有足够灵活性的宏观经济模型，以便能够利用和组织所有这些信息。然而，这些模型可能非常复杂，以至于它们变成了不可行的黑匣子，因此很难知道结果来自哪里，以及它们对假设的敏感程度。该项目开发了数据驱动理论，通过将计算模型的灵活性与理论模型的透明性相结合来聚合粒度数据。 然后，该项目将这些理论应用于新获得的微观数据集，以研究重要的宏观经济问题，如生产率，经济增长，贸易和不平等。 该项目将为非新古典生产的经济体开发和应用新的聚合结果，如固定成本，规模报酬递增，进入-退出利润率和不连续性。这些问题通常在国民收入和增长的衡量和分析中被抽象出来。这个项目将扩展经典的聚合定理，允许非线性，扭曲，异构代理，异构企业，生产网络和国际贸易进一步与非新古典，非凸，非连续生产结构的环境。该项目将把理论成果应用于最近可用的微观层面的数据集，这些数据集可以对丰富的理论可能性进行规范和结构化。该奖项反映了NSF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。

AbstractOne of the biggest changes in economics in the 21st century is the increasing availability of comprehensive, high quality, micro-level data on households and firms. To utilize all this data meaningfully, we need macroeconomic models with enough flexibility to be able to leverage and organize all this information. However, such models can be so complex that they turn into unworkable black boxes, whereby it becomes difficult to know where the results are coming from, and how sensitively they depend on the assumptions. This project develops data-driven theory which aggregates granular data by combining the flexibility of computational models with the transparency of theoretical ones. The project then applies these theories to newly-available micro datasets to investigate important macroeconomic issues such as productivity, economic growth, trade and inequality. This project will develop and apply new aggregation results for economies with non-neoclassical production like fixed costs, increasing returns to scale, entry-exit margins, and discontinuities. These are issues that are typically abstracted away from in both the measurement and analysis of national income and growth. This project will extend classical aggregation theorems that allow for non-linearities, distortions, heterogeneous agents, heterogeneous firms, production networks, and international trade further to environments with non-neoclassical, non-convex, and non-continuous production structures. The project will apply the theoretical results to recently available micro-level datasets that can discipline and put structure on the rich range of theoretical possibilities.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.