| Project/Area Number |
14530031
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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 | Kyoto Institute of Technology |
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Principal Investigator |
HITOMI Kohtaro Kyoto Institute of Technology, Architecture and Design, Assistant professor, 工芸学部, 助教授 (00283680)
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| Project Period (FY) |
2002 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2004: ¥200,000 (Direct Cost: ¥200,000)
Fiscal Year 2003: ¥200,000 (Direct Cost: ¥200,000)
Fiscal Year 2002: ¥500,000 (Direct Cost: ¥500,000)
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| Keywords | semiparametric / GMM / statistical tests / exponential tilting / Granger causality / 推定方法 / バイアス / 分散 / 経験尤度推定量 / Exponential Tilting estimator / Empirical Likelihood Estimator / 小標本 / Empirical Likelihood / Exponential Tilting / Small sample bics / number of Moment Conditions |
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| Research Abstract |
This report investigated three topics. The first paper compares conventional GMM estimator to Empirical Likelihood(EL) and Exponential Tilting estimators when the number of moment conditions increases with the number of observations. The main findings of the experiments show the following. Small sample biases of EL and ET are considerably smaller than GMM if the number of moment conditions is less than 10% of the number of observations. When the number of the moment conditions exceed 10% of the number of observations, small sample biases of EL and ET increase almost linearly as the number of moment conditions increase and the slopes are greater than the slope of GMM. The small sample bias of bias corrected GMM (Newey and Smith 2001) is always the same or smaller than the biases of other estimators. The standard deviations of GMM and bias corrected GMM estimators are decreased as the number of moment conditions is increased. The standard deviations of EL and ET estimators, however, are increased when the number of moment conditions exceeds 10% of the number of observations.
The second paper investigated Granger causality. We developed root-n consisitent nonparametric Granger causality test.
Third paper analyzed a paradoxical phenomenon of semiparametric model that some semiparametric estimators are more efficient when infinite dimensional nuisance parameters are unknown. This paper examined the structure of the paradox. The necessary and sufficient condition of the paradox is presented and a simple sufficient condition is derived. In addition, two examples of semiparametric estimators are examined.
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