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

73 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

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10.1080/00949659608811737

Cite this

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A significance test for classifying ARMA models

Abstract

Keywords

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