Nonlinear and nonstationary models in econometric analysis
| Project/Area Number |
08630023
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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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 | University of Tokyo |
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Principal Investigator |
YAJIMA Yoshihiro University of Tokyo, Faculty of Economics, Professor, 大学院・経済学研究科, 教授 (70134814)
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| Co-Investigator(Kenkyū-buntansha) |
KUNITOMO Naoto University of Tokyo, Faculty of Economics, Professor, 大学院・経済学研究科, 教授 (10153313)
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| Project Period (FY) |
1996 – 1997
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| Project Status |
Completed (Fiscal Year 1997)
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| Budget Amount *help |
¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 1997: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1996: ¥1,000,000 (Direct Cost: ¥1,000,000)
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| Keywords | nonstationary model / long-memory model / seasonal adjustment method / nonlinear model / missing observations / autoregressive model / sample autocorrelation / 単位根検定 / 転換時系列モデル |
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| Research Abstract |
Yajima investigated an effect of missing observations on estimation of nonstationary unit root processes and stationary long memory models.
He considered the two estimations for unit root processe. The first one is a Yule-Walker type estimator and the second one is a least-squares type estimator.
He derived the limiting distributions of these estimators. Next he introduced the third estimator, a sample-correlation coefficient estimator. Then he clarified their asymptotic difference if we apply them to estimate the autocorrelation function of both short-memory and long-memory stationary models.
Kunitomo considered X-11-ARIMA,a seasonal adjustment method which has been recently developed by Bureau of Census, Department of Commerce in U.S.A.He clarified its theoretical properties and topics which should be solved in future. He also proved the limiting distribution of the maximum likelihood estimator of a simultaneous switching autoregressive model proposed by himself.
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Report
(3 results)
Research Products
(15 results)
