.png){kind=icon}
返回
Model identification for infinite variance autoregressive processes
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jeconom.2012.08.009.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We consider model identification for infinite variance autoregressive time series processes. It is shown that a consistent estimate of autoregressive model order can be obtained by minimizing Akaike's information criterion, and we use all-pass models to identify noncausal autoregressive processes and estimate the order of noncausality (the number of roots of the autoregressive polynomial inside the unit circle in the complex plane). We examine the performance of the order selection procedures for finite samples via simulation, and use the techniques to fit a noncausal autoregressive model to stock market trading volume data. (C) 2012 Elsevier B.V. All rights reserved.
Keyword:
Akaike's information criterion
All-pass models
Autoregressive processes
Infinite variance
Noncausal
期刊
IF:
4
论文数:
5.3K
被引数:
3.0W
机构
引用论文
Synthesis and characterization of poly(4-mercaptobenzoyl) whiskers from S-acetyl-4-mercaptobenzoic acid.png)
Polymer
PolymerIF0
PREAI
分享收藏
分享收藏Chemical vapor transport of CuInS2: Correlation of growth induced defect structure and photoactivity
PREAI
分享收藏分享收藏
查看更多内容