| 研究課題/領域番号 |
22K01394
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| 研究種目 |
基盤研究(C)
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| 配分区分 | 基金 |
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| 応募区分 | 一般 |
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| 審査区分 |
小区分07010:理論経済学関連
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| 研究機関 | 慶應義塾大学 |
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研究代表者 |
大津 敬介 慶應義塾大学, 商学部(三田), 教授 (50514527)
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| 研究分担者 |
稲葉 大 専修大学, 経済学部, 教授 (50611315)
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| 研究期間 (年度) |
2022-04-01 – 2025-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
4,160千円 (直接経費: 3,200千円、間接経費: 960千円)
2024年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
2023年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
2022年度: 1,560千円 (直接経費: 1,200千円、間接経費: 360千円)
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| キーワード | 景気循環会計 / 非線形解法 / コロナショック |
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| 研究開始時の研究の概要 |
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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| 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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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
We have developed business cycle models and data to be analyzed using the proposed non-linear solution methods. The Co-Investigator has already presented preliminary results in an international conference and published a working paper.
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| 今後の研究の推進方策 |
In the third year, we will focus on comparing business cycle accounting results between linear and non-linear methods. We will organize a workshop to disseminate our findings.
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