LADE-Based Inference for ARMA Models With Unspecified and Heavy-Tailed Heteroscedastic Noises
文献类型：期刊论文
| 作者 | Zhu, Ke1 ; Ling, Shiqing2
|
| 刊名 | JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
 |
| 出版日期 | 2015-06-01 |
| 卷号 | 110期号:510页码:784-794 |
| 关键词 | ARMA(p, q) models Asymptotic normality G/ARCH noises Heavy-tailed noises LADE Random weighting approach Self-weighted LADE Sign-based portmanteau test Strong consistency |
| ISSN号 | 0162-1459 |
| DOI | 10.1080/01621459.2014.977386 |
| 英文摘要 | This article develops a systematic procedure of statistical inference for the auto-regressive moving average (ARMA) model with unspecified and heavy-tailed heteroscedastic noises. We first investigate the least absolute deviation estimator (LADE) and the self-weighted LADE for the model. Both estimators are shown to be strongly consistent and asymptotically normal when the noise has a finite variance and infinite variance, respectively. The rates of convergence of the LADE and the self-weighted LADE are n(-1/2), which is faster than those of least-square estimator (LSE) for the ARMA model when the tail index of generalized auto-regressive conditional heteroskedasticity (GARCH) noises is in (0, 4], and thus they are more efficient in this case. Since their asymptotic covariance matrices cannot be estimated directly from the sample, we develop, the random weighting approach for statistical inference under this nonstandard case. We further propose a novel sign-based portmanteau test for model adequacy. Simulation study is carried out to assess the performance of our procedure and one real illustrating example is given. Supplementary materials for this article are available online. |
| 资助项目 | Hong Kong Research Grants Commission[HKUST641912] ; Hong Kong Research Grants Commission[HKUST603413] ; National Natural Science Foundation of China[11201459] ; National Natural Science Foundation of China[11371354] ; Academy of Mathematics and System Science, Chinese Academy of Sciences[2014-cjrwlzx-zk] ; Key Laboratory of RCSDS, Chinese Academy of Sciences |
| WOS研究方向 | Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:000357437300025 |
| 出版者 | AMER STATISTICAL ASSOC |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/20277]  |
| 专题 | 国家数学与交叉科学中心 |
| 通讯作者 | Zhu, Ke |
| 作者单位 | 1.Chinese Acad Sci, Inst Appl Math, Beijing, Peoples R China 2.Hong Kong Univ Sci & Technol, Dept Math, Kowloon, Hong Kong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Ke,Ling, Shiqing. LADE-Based Inference for ARMA Models With Unspecified and Heavy-Tailed Heteroscedastic Noises[J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION,2015,110(510):784-794. |
| APA | Zhu, Ke,&Ling, Shiqing.(2015).LADE-Based Inference for ARMA Models With Unspecified and Heavy-Tailed Heteroscedastic Noises.JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION,110(510),784-794. |
| MLA | Zhu, Ke,et al."LADE-Based Inference for ARMA Models With Unspecified and Heavy-Tailed Heteroscedastic Noises".JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 110.510(2015):784-794. |
入库方式： OAI收割
来源：数学与系统科学研究院
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