On scalable ARMA models
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909232109977600 |
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| author | Lin, Yuchang Li, Wenyu Zhu, Qianqian Li, Guodong |
| author_facet | Lin, Yuchang Li, Wenyu Zhu, Qianqian Li, Guodong |
| contents | This paper considers both the least squares and quasi-maximum likelihood estimation for the recently proposed scalable ARMA model, a parametric infinite-order vector AR model, and their asymptotic normality is also established. It makes feasible the inference on this computationally efficient model, especially for economic and financial time series. An efficient block coordinate descent algorithm is further introduced to search for estimates, and a Bayesian information criterion with selection consistency is suggested for model selection. Simulation experiments are conducted to illustrate their finite sample performance, and a real application on six macroeconomic indicators illustrates the usefulness of the proposed methodology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12825 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On scalable ARMA models Lin, Yuchang Li, Wenyu Zhu, Qianqian Li, Guodong Methodology 62M10, 62F12 This paper considers both the least squares and quasi-maximum likelihood estimation for the recently proposed scalable ARMA model, a parametric infinite-order vector AR model, and their asymptotic normality is also established. It makes feasible the inference on this computationally efficient model, especially for economic and financial time series. An efficient block coordinate descent algorithm is further introduced to search for estimates, and a Bayesian information criterion with selection consistency is suggested for model selection. Simulation experiments are conducted to illustrate their finite sample performance, and a real application on six macroeconomic indicators illustrates the usefulness of the proposed methodology. |
| title | On scalable ARMA models |
| topic | Methodology 62M10, 62F12 |
| url | https://arxiv.org/abs/2402.12825 |