| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Outline of Final Research Achievements |
The theory of the locally stationary time series factor models for dimension reduction for large-scale financial data was prepared, and then the statistical asymptotic theory of the finite-dimensional locally stationary time series factor model was established. A simple eigenvalue analysis of a nonnegative definite multiplied covariance matrix was used to give estimators for both the number of factors and the factor loadings. Since many existing methods only examine the consistency of estimators, the results are uniform in the stationary and non-stationary cases. Therefore, we could not judge how non-stationarity affects the estimator. By investigating the asymptotic variance of the estimator, we clarifyiedthe difference in the properties of the proposed estimator under the assumption of the locally stationary time series factor model and under the assumption of the stationary time series factor model.
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