MODEL IDENTIFICATION FOR TIME SERIES WITH DEPENDENT INNOVATIONS

被引：6

作者：

Chen, Shuhao ^{[1
]}

Min, Wanli ^{[2
]}

Chen, Rong ^{[3
]}

机构：

[1] Bank Amer Merrill Lynch, New York, NY 10036 USA

[2] Google, Mountain View, CA 94043 USA

[3] Rutgers State Univ, Dept Stat, Piscataway, NJ 08854 USA

来源：

STATISTICA SINICA | 2013年 / 23卷 / 02期

基金：

美国国家科学基金会;

关键词：

ACF; EACF; GARCH; PACF; order determination; time series; uncorrelated but dependent errors; unit-root test;

D O I：

10.5705/ss.2010.219

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This paper investigates the impact of dependent but uncorrelated innovations (errors) on the traditional autoregressive moving average model (ARMA) order determination schemes such as autocorrelation function (ACF), partial autocorrelation function (PACF), extended autocorrelation function (EACF), and the unit-root test. The ARMA models with iid innovations have been studied extensively and are well-posed, but their properties with dependent but uncorrelated innovations are relatively less studied. In the presence of such innovations, we show that the ACF, PACF, and EACF are significantly impacted while the unit-root test is not affected. We also propose a new order determination scheme to address those impacts for analyzing time series with uncorrelated innovations.

引用

页码：873 / 899

页数：27

共 18 条

[1] NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION [J].