2023 Fiscal Year Final Research Report
GDP forecast considering statistic reform - Nowcasting by big data
| Project/Area Number |
19K01680
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 07040:Economic policy-related
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| Research Institution | Atomi University |
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Principal Investigator |
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| Project Period (FY) |
2019-04-01 – 2024-03-31
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| Keywords | 統計改革 / 県内総生産 / 経済統計 / EBPM |
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| Outline of Final Research Achievements |
I created the monthly real GDP for each prefecture and analyzed the impact of the state of emergency declarations during the spread of COVID-19. The monthly real GDP was estimated from the production side using indices such as the Industrial Production Index and the Tertiary Industry Activity Index. As another approach, I devised a method to estimate real GDP from the Economic Trend Index. I utilized a cointegration relationship between GDP and the Economic Trend Index.
Regarding statistical reforms, I analyzed the distribution of GDP and the quick release of corporate statistics. To improve the statistical literacy of civil servants, I conducted lectures at the Ministry of Internal Affairs and Communications, the Ministry of Finance, and the Ministry of Economy, Trade and Industry. These were presented as videos and in my work titled "Learning Econometrics through Regression Analysis - Understanding the Economy with Excel."
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| Free Research Field |
経済政策
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| Academic Significance and Societal Importance of the Research Achievements |
各都道府県が発表する県内総生産は発表が遅くしかも年次統計である。現状では作成してもあまり活用されていない。このため、本研究で提案した、発表が早く、月次で推定できる「都道府県別月次実質GDP」は、県内総生産を補完するものとして社会的意義がある。特に、新型コロナウイルスの感染拡大期や東日本大震災などの災害時は、日本全体の統計ではあまり意味がなく、都道府県別で迅速に発表されてこそ意味を持つ。
学術的には、機械学習の手法であるクラスター分析を使ったり、緊急事態宣言の効果を調べるために、人流データや感染データを使ったりしたとに意義がある。
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