A significance test for classifying ARMA models

Elizabeth Ann Maharaj

doi:10.1080/00949659608811737

A significance test for classifying ARMA models

Elizabeth Ann Maharaj

Econometrics & Business Statistics

Research output: Contribution to journal › Article › Research › peer-review

74 Citations (Scopus)

Abstract

Given that the Euclidean distance between the parameter estimates of autoregressive expansions of autoregressive moving average models can be used to classify stationary time series into groups, a test of hypothesis is proposed to determine whether two stationary series in a particular group have significantly different generating processes. Based on this test a new clustering algorithm is also proposed. The results of Monte Carlo simulations are given.

Original language	English
Pages (from-to)	305-331
Number of pages	27
Journal	Journal of Statistical Computation and Simulation
Volume	54
Issue number	4
DOIs	https://doi.org/10.1080/00949659608811737
Publication status	Published - 1 Jan 1996

Keywords

ARMA models
Significance test
Time series

Access to Document

10.1080/00949659608811737

Cite this

@article{f1fda5d265a74af3893958bed57ec30c,

title = "A significance test for classifying ARMA models",

abstract = "Given that the Euclidean distance between the parameter estimates of autoregressive expansions of autoregressive moving average models can be used to classify stationary time series into groups, a test of hypothesis is proposed to determine whether two stationary series in a particular group have significantly different generating processes. Based on this test a new clustering algorithm is also proposed. The results of Monte Carlo simulations are given.",

keywords = "ARMA models, Significance test, Time series",

author = "Maharaj, \{Elizabeth Ann\}",

year = "1996",

month = jan,

day = "1",

doi = "10.1080/00949659608811737",

language = "English",

volume = "54",

pages = "305--331",

journal = "Journal of Statistical Computation and Simulation",

issn = "0094-9655",

publisher = "Taylor \& Francis",

number = "4",

}

TY - JOUR

T1 - A significance test for classifying ARMA models

AU - Maharaj, Elizabeth Ann

PY - 1996/1/1

Y1 - 1996/1/1

N2 - Given that the Euclidean distance between the parameter estimates of autoregressive expansions of autoregressive moving average models can be used to classify stationary time series into groups, a test of hypothesis is proposed to determine whether two stationary series in a particular group have significantly different generating processes. Based on this test a new clustering algorithm is also proposed. The results of Monte Carlo simulations are given.

AB - Given that the Euclidean distance between the parameter estimates of autoregressive expansions of autoregressive moving average models can be used to classify stationary time series into groups, a test of hypothesis is proposed to determine whether two stationary series in a particular group have significantly different generating processes. Based on this test a new clustering algorithm is also proposed. The results of Monte Carlo simulations are given.

KW - ARMA models

KW - Significance test

KW - Time series

UR - https://www.scopus.com/pages/publications/0002115641

U2 - 10.1080/00949659608811737

DO - 10.1080/00949659608811737

M3 - Article

AN - SCOPUS:0002115641

SN - 0094-9655

VL - 54

SP - 305

EP - 331

JO - Journal of Statistical Computation and Simulation

JF - Journal of Statistical Computation and Simulation

IS - 4

ER -

A significance test for classifying ARMA models

Abstract

Keywords

Access to Document

Other files and links

Cite this