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

页码：369 / 394

页数：26

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