| Project/Area Number |
22K13377
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 07030:Economic statistics-related
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| Research Institution | Osaka University |
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Principal Investigator |
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| Project Period (FY) |
2022-04-01 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2024: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2023: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2022: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | Copula / Factor model / High dimension / Sparsity / Asymptotic theory / Copulas / Factor models / Time series / Multivariate modelling |
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| Outline of Research at the Start |
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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| Outline of Annual Research Achievements |
The paper "Sparse M-estimators in semi-parametric copula models", co-authored with Prof Fermanian, has been accepted for publication at Bernoulli in 2023 and is forthcoming in 2024. The paper answered the issues inherent to copula models: pseudo-observations; unbounded copula-based objective functions; explosive number of parameters. We specified a suitable penalized M-estimator framework for copulas and derived the asymptotic properties.
The paper "Sparse factor models of high dimension", co-authored with Prof Terada, is currently submitted at an econometrics journal: we devised a sparsity-based estimation framework for the factor loading matrix taking into account the rotational indeterminacy and derived the asymptotic properties.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
The research is moving forward: one paper published in Bernoulli; one paper currently submitted at an econometrics journals; one research project on stochastic volatility models is about to be completed.
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| Strategy for Future Research Activity |
The project "Factor Multivariate Stochastic Volatility Models", co-authored with Prof Asai, is about to be completed and will be submitted at an econometrics journal. The key idea is to integrate factors in the Multivariate Stochastic Volatility (MSV) model. We propose to estimate the latent factors using the estimators of the factor decomposition and then specify a multivariate state space representation of the latent volatility of the factors (not the observed random vector, which can be high-dimensional). Theoretical analysis of the proposed method: asymptotic properties derived under moment conditions.
The replication package will be made publicly available for the sake of transparancy.
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