A new approach to time-series econometric analysis with point processes
| Project/Area Number |
17H02513
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Economic statistics
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| Research Institution | Meiji University |
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Principal Investigator |
Kunitomo Naoto 明治大学, 政治経済学部, 特任教授 (10153313)
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| Co-Investigator(Kenkyū-buntansha) |
大屋 幸輔 大阪大学, 経済学研究科, 教授 (20233281)
佐藤 整尚 東京大学, 大学院経済学研究科(経済学部), 准教授 (60280525)
栗栖 大輔 東京工業大学, 工学院, 助教 (70825835)
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| Project Period (FY) |
2017-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥12,220,000 (Direct Cost: ¥9,400,000、Indirect Cost: ¥2,820,000)
Fiscal Year 2020: ¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2019: ¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2018: ¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2017: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
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| Keywords | 時系列分析 / 非定常時系列 / 点過程 / マクロ時系列 / 高頻度金融時系列 / Levy過程 / SIMLフィルタリング / 時系列計量分析 / 点過程アプローチ / マクロ経済時系列 / 高頻度金融データ / 時系列フィルタリング / 統計的時系列分析の理論と応用 / マクロ経済データと金融データ / 点過程(ジャンプ過程)と確率過程 / 計量経済学 / 時系列解析 / 点確率過程 |
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| Outline of Final Research Achievements |
This research project has studied important issues of econometric methods based on stochastic point processes. It investigated some statistical modelling of multivariate financial time series and macro-economic time, which has been then applied to measure economic risk analysis including financial risk and insurance risk We have studied various scientific and systematic aspects of point processes and non-stationary multivariate economic time series. The project members have presented research results at various international as well as domestic academic meetings and published some books and many academic papers.
We have developed a new filtering method called the SIML (separating information maximum likelihood) method for high frequency financial data and multivariate macro- economic data. We have shown that the new method gives a new way to handle the filtering problem of macro-economic time series and also a way to find some hidden factors in high frequency financial time series
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| Academic Significance and Societal Importance of the Research Achievements |
これまで知られていた計量分析法では社会や経済で時々起きる現象の分析は十分とは言えず、この研究プロジェクトにより点過程アプローチの重要性と限界が明らかになったことは今後のさらなる研究の基礎となることが期待される。また近年の社会・経済では事前には予想できない時々しか起きない大きな変動の理解がすすみ、対処法の設計に資すると考えられる。
なお報告書に研究プロジェクト参加者の佐藤の応用例「コロナ感染の逐次予測法」について説明した。
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Report
(5 results)
Research Products
(33 results)
