| Project/Area Number |
15330040
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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 | Kyoto University |
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Principal Investigator |
NISHIYAMA Yoshihiko Kyoto University, Institute of economic research, professor, 経済研究所, 教授 (30283378)
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| Co-Investigator(Kenkyū-buntansha) |
MORIMUNE Kimio Kyoto University, Graduate school of economics, professor, 経済研究所, 教授 (20109078)
TANIZAKI Hisashi Kobe University, Graduate school of economics, professor, 経済学研究科, 教授 (60248101)
HITOMI Kohtaro Kyoto institute of technology, department of engineering, associate professor, 工芸学部, 助教授 (00283680)
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| Project Period (FY) |
2003 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥6,800,000 (Direct Cost: ¥6,800,000)
Fiscal Year 2005: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2004: ¥2,400,000 (Direct Cost: ¥2,400,000)
Fiscal Year 2003: ¥3,300,000 (Direct Cost: ¥3,300,000)
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| Keywords | Semiparametric methods / EL estimation / Moment conditions / Bootstrap / Bootstrap / Empirical likelihood / First order asymptotics / GMM / Heterogeneity test / Higher order asymptotics / Semiparametric estimation / Semiparametric Paradox / 経験尤度関数 / 漸近理論 / シュミレーション / 時系列解析 / 因果性 / ジャンプ付拡散過程 / セミパラメトリック推定 / シミュレーション |
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| Research Abstract |
Hitomi studied the bias of the empirical likelihood estimators by a Monte Carlo simulation, where standard linear model setup is used as the DGP, also comparing with the ET and GMM estimators.
Hitomi and Nishiyama (2005) investigated how a puzzling situation in terms of asymptotic variance occurs in semiparametric settings. It is normally the case that if nuisance parameters are estimated and plugged in, the asymptotic variance of the parameters of interest tends to be large, but there are cases the opposite is true. They provided a necessary and sufficient condition when it happens.
Nishiyama and Robinson (2005) obtained a valid Edgeworth expansion of order two for the semiparametric averaged derivatives and checked the difference from the Bootstrap distribution.
Liu and Nishiyama studied the performance of empirical likelihood estimators of diffusion processes with jumps by simulation. It is shown that EL performs better than the GMM for some parameters.
Nishiyama, Liu and Sueishi (2005) compared EL, GMM estimators for semiparametric models which include nonparametric functionals in the moment condition.
Morimune and Hoshino proposed to use Bootstrap when data includes heterogeneous observations.
Tanizaki et.al. studied small sample power properties of Cressie-Read power divergence test.
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