Forecasting Using Non-linear Multivariate Time Series Models with Bayesian Stochastic Search Variable Selection Method and its Application to Macroeconmics
| Project/Area Number |
17K03661
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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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| Research Field |
Economic statistics
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| Research Institution | University of the Ryukyus |
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Principal Investigator |
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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 |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | 計量経済学 / 時系列分析 / 多変量時系列 / ベイズ統計学 / MCMC / 多変量時系列分析 / ベイズ法 / マルコフ連鎖モンテカルロ法 / 非線形時系列 / ベイズ計量経済学 |
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| Outline of Final Research Achievements |
A VAR (Vector Auto Regression) model is often used for empirical studies for macroeconomic analysis or forecasting macroeconomic variables. However, one of the problem of using a VAR model is that VAR model often contains too many variables of which are insignificant. In this research, I examine the forecasting performance of Bayesian SSVS (Stochastic search variable selection) method to remove insignificant variables in the model for model selection.I showed that the SSVS method improve the performance of the time series forecasting by using aritificially generated stationary or non-stationary data.
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| Academic Significance and Societal Importance of the Research Achievements |
研究成果の学術的意義として、多変量時系列モデルにおいてより高い予測精度をもたらすSSVS法の利便性を示したことにある。このメソッドは汎用性があり多くのモデルに応用できるので、これからの時系列予測や計量分析に役立つという意義がある。
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Report
(5 results)
Research Products
(3 results)
