2022 Fiscal Year Final Research Report
Parsimonious statistical modelling for high-dimensional problems
| Project/Area Number |
19K23193
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Multi-year Fund |
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| Review Section |
0107:Economics, business administration, and related fields
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| Research Institution | Osaka University |
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Principal Investigator |
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| Project Period (FY) |
2019-08-30 – 2023-03-31
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| Keywords | Copulas / Factor models / Financial econometrics / Sparsity |
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| Outline of Final Research Achievements |
The research was devoted to the sparse modelling of multivariate models and to the development of statistical methods to fix the curse of dimensionality. The sparse approach aimed to improve the precision of the M-estimators and to improve the prediction performances. Three multivariate models were under consideration: multivariate stochastic volatility models (financial econometrics literature); factor models; copula models. For each of these models, we specified a sparsity-based estimation framework, derived the corresponding theoretical properties (finite/large sample properties) and illustrated the relevance of the proposed method through numerical experiments. In particular, the specification of a suitable M-estimation criterion was key to allow for fast-solving implementation methods. We could apply the sparse modelling to high-dimensional random vectors (e.g., financial data) and obtain better out-of-sample performances compared to non-sparse methods.
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| Free Research Field |
Econometrics/Statistics
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| Academic Significance and Societal Importance of the Research Achievements |
The curse of dimensionality is the main drawback inherent to most multivariate models due to the explosive number of parameters. The research main purpose was to fix this curse, provide methods to efficiently model high-dimensional vectors and improve the prediction performances.
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