| 研究課題/領域番号 |
22K01394
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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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| 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.
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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 completed the literature review and agreed on the non-linear solution approach as scheduled.
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| 今後の研究の推進方策 |
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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| 次年度使用額が生じた理由 |
Given the smooth progress in Academic Year 2022, we managed to complete our tasks without hiring research assistants or purchasing additional equipment, which was our initial plan. Instead, we were planning to use the budget for overseas traveling to meet specialists in the field and make progress ahead of schedule. However, we could not travel during Academic Year 2022 due to the long-lasting influence of the covid-19 pandemic. Therefore, we will reallocate the budget to Academic Year 2023 for overseas travel expenses.
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