Project/Area Number |
22K01394
|
Research Category |
Grant-in-Aid for Scientific Research (C)
|
Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 07010:Economic theory-related
|
Research Institution | Keio University |
Principal Investigator |
大津 敬介 慶應義塾大学, 商学部(三田), 教授 (50514527)
|
Co-Investigator(Kenkyū-buntansha) |
稲葉 大 専修大学, 経済学部, 教授 (50611315)
|
Project Period (FY) |
2022-04-01 – 2025-03-31
|
Project Status |
Granted (Fiscal Year 2022)
|
Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2024: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2023: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2022: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
|
Keywords | 景気循環会計 / 非線形解法 / コロナショック |
Outline of Research at the Start |
In general, non-linear solution methods of dynamic stochastic general equilibirum macroeconomic models vary across their performances in terms of accuracy and computational burden. In this project, we will quantitatively assess the tradeoff between computational efficiency and accuracy across several common non-linear methods applied to the business cycle accounting model that decomposes business cycles into four representative shocks using data of OECD countries during the Covid-19 pandemic featuring large macroeconomic shocks.
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Outline of Annual Research Achievements |
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.
|
Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
We have completed the literature review and agreed on the non-linear solution approach as scheduled.
|
Strategy for Future Research Activity |
In Academic Year 2023, we will conduct the quantitative analysis. The Co-Investigator will construct a quarterly data set of output, consumption, investment and labor supply for the 2015Q1-2022Q4 period and will also conduct Business Cycle Accounting using the linear method, finite element method and the parameterized expectation algorithm. The Principal Investigator will program the designed method of Business Cycle Accounting with Artificial Neural Networks. Through Business Cycle Accounting, we can decompose the downturn in output during the Covid-19 crisis into the effects of efficiency, labor, investment, and government wedges. Furthermore, we will compare the computational time and the size of approximation errors across the solution methods following Fernandez-Villaverde, Rubio-Ramirez and Schorfheide (2016). In Academic Year 2024, we will organize a workshop to gain feedback from researchers in the field of non-linear solution methods. We will also attend international conferences to disseminate our business cycle accounting results and policy implications on the Covid-19 Crisis in OECD economies.
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