Selecting nonlinear time series models using information criteria

被引：19
|
作者：
Psaradakis, Zacharias [1 ]
Sola, Martin [1 ]
Spagnolo, Fabio [2 ]
Spagnolo, Nicola [2 ]
机构：
[1] Univ London, London WC1E 7HU, England
[2] Brunel Univ, Uxbridge UB8 3PH, Middx, England
来源：
JOURNAL OF TIME SERIES ANALYSIS | 2009年 / 30卷 / 04期
关键词：
Complexity-penalized likelihood criteria; nonlinear models; Monte Carlo experiments; PRINCIPLE;
D O I：
10.1111/j.1467-9892.2009.00614.x
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
vertical bar This article considers the problem of selecting among competing nonlinear time series models by using complexity-penalized likelihood criteria. An extensive simulation study is undertaken to assess the small-sample performance of several popular criteria in selecting among nonlinear autoregressive models belonging to some families that have been popular with practitioners.
引用
收藏
页码：369 / 394
页数：26
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