| 研究実績の概要 |
The main aim of this project is to assess the tradeoff between computational efficiency and accuracy across linear and non-linear solution methods. We will analyze the Covid-19 crisis because economic fluctuations are large, which will lead to significant approximation errors in the linear method. We apply the Business Cycle Accounting framework of Chari, Kehoe and McGrattan (2007) because it is a useful method to decomposes economic fluctuations into representative sources. However, it is challenging to solve this model with non-linear methods due to the computational burden. Therefore, this project will provide new insights not only on the sources of the latest economic crisis, but also tradeoff between computational efficiency and accuracy.
In the first year, we reviewed the literature on solution methods to design our Business Cycle Accounting method. In particular, the Co-Investigator reviewed the finite element method and the parameterized expectation algorithm. The Principal Investigator reviewed the machine learning literature on Artificial Neural Networks. We have jointly designed the non-linear solution method for Business Cycle Accounting with Artificial Neural Networks at a conceptual level.
In the second year, we have extended the Business Cycle Accounting method to various settings. The Principal Investigator applied the method to monthly business cycle data to investigate the short run impact of Covid-19 in Japan. The-Co-Investigator implemented the framework to a two-agent model to identify the sources of business cycles and inequality.
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