Model selection for vector autoregressive processes using broken adaptive ridge
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Wiley Open Access
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| Format: | Artículo Open Access |
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2025
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| _version_ | 1867020193869332480 |
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| author | Jinzhi Huang Bingzhao Li Xingzhong Xu |
| author_facet | Jinzhi Huang Bingzhao Li Xingzhong Xu Jinzhi Huang Bingzhao Li Xingzhong Xu |
| collection | Wiley Open Access |
| contents | Model selection for vector autoregressive processes using broken adaptive ridge Jinzhi Huang Bingzhao Li Xingzhong Xu Statistica Neerlandica ABSTRACT We consider a sparse vector autoregressive model with divergent lag order. As a linear model, all its explanatory variables are lagged responses such that there may be high correlation between them. Hence, the broken adaptive ridge procedure is employed for its iterative algorithm, which starts with a ridge estimator as the initial one. We obtained parameter estimation and model selection simultaneously by the procedure named VBAR in this paper. Theoretically, we established that the VBAR procedure is consistent for model selection and an oracle for parameter estimation. Simulations demonstrate the superiority of the VBAR procedure over Lasso, Adaptive Lasso, and SCAD procedures. Additionally, the Google Flu Trends data are analyzed by the VBAR procedure, which gives a more sparse model and more accurate predictions compared with other procedures. 10.1111/stan.70021 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1111/stan.70021 |
| format | Artículo Open Access |
| id | wiley_oa_10_1111_stan_70021 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Model selection for vector autoregressive processes using broken adaptive ridge Jinzhi Huang Bingzhao Li Xingzhong Xu Statistica Neerlandica Model selection for vector autoregressive processes using broken adaptive ridge Jinzhi Huang Bingzhao Li Xingzhong Xu Statistica Neerlandica ABSTRACT We consider a sparse vector autoregressive model with divergent lag order. As a linear model, all its explanatory variables are lagged responses such that there may be high correlation between them. Hence, the broken adaptive ridge procedure is employed for its iterative algorithm, which starts with a ridge estimator as the initial one. We obtained parameter estimation and model selection simultaneously by the procedure named VBAR in this paper. Theoretically, we established that the VBAR procedure is consistent for model selection and an oracle for parameter estimation. Simulations demonstrate the superiority of the VBAR procedure over Lasso, Adaptive Lasso, and SCAD procedures. Additionally, the Google Flu Trends data are analyzed by the VBAR procedure, which gives a more sparse model and more accurate predictions compared with other procedures. 10.1111/stan.70021 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Model selection for vector autoregressive processes using broken adaptive ridge |
| topic | Statistica Neerlandica |
| url | https://onlinelibrary.wiley.com/doi/10.1111/stan.70021 |