研究課題/領域番号 |
22K13377
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研究種目 |
若手研究
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配分区分 | 基金 |
審査区分 |
小区分07030:経済統計関連
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研究機関 | 大阪大学 |
研究代表者 |
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研究期間 (年度) |
2022-04-01 – 2025-03-31
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研究課題ステータス |
交付 (2022年度)
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配分額 *注記 |
4,420千円 (直接経費: 3,400千円、間接経費: 1,020千円)
2024年度: 650千円 (直接経費: 500千円、間接経費: 150千円)
2023年度: 1,560千円 (直接経費: 1,200千円、間接経費: 360千円)
2022年度: 2,210千円 (直接経費: 1,700千円、間接経費: 510千円)
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キーワード | Asymptotic theory / Copulas / Factor models / Sparsity / Time series / High dimension / Multivariate modelling |
研究開始時の研究の概要 |
The research is devoted to the development of sparsity based estimation procedure to tackle the curse of dimensionality. A significant work will be dedicated to the theoretical properties (large sample, finite sample) and the applications (simulations, real world data) to illustrate the relevance of the proposed sparse methods. We expect to greatly enhance the prediction performances of the fitted sparse models. The key challenge is to break the curse of dimensionality inherent to multivariate models.
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研究実績の概要 |
The research focused on the sparse modelling of multivariate models and the development of parsimonious statistical methods. The sparse modelling aimed to improve the prediction accuracy and the precision of the estimators. The main part of the research was devoted to the derivation of the theoretical properties of such sparse techniques (mainly large sample analysis) and to the assessment of the empirical performances of illustrate the relevance of the sparse modelling. We could develop fast solving algorithms and showed that the sparse approach provides good theoretical properties. We could model high-dimensional random vectors and fix the curse of dimensionality.
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現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
The research is having a good progress: one paper on the sparse modelling and identification of Structural Vector Autoregression has been published. One paper on the sparse modelling of copulas is currently revised and resubmitted.
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今後の研究の推進方策 |
We will apply the sparse modelling to two multivariate models: copulas within the semi-parametric setting; factor models, where the penalization will be applied to the factor loading matrix. We will derive the conditions for the oracle property (for both fixed and diverging dimension cases) and apply the method to financial data (portfolio allocation). We expect the following issues: - for copulas: the non-parametric transformation on the marginal distributions will be the most difficult problem; the theoretical properties will significantly depend on this transformation. - for factor models: the treatment of the rotational indeterminacy while fostering sparsity will be the most difficult task.
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