A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples

Liu, Shen & Maharaj, Elizabeth Ann (2013) A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples. Computational Statistics & Data Analysis, 60, pp. 32-49.

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Abstract

A new test of hypothesis for classifying stationary time series based on the bias-adjusted estimators of the fitted autoregressive model is proposed. It is shown theoretically that the proposed test has desirable properties. Simulation results show that when time series are short, the size and power estimates of the proposed test are reasonably good, and thus this test is reliable in discriminating between short-length time series. As the length of the time series increases, the performance of the proposed test improves, but the benefit of bias-adjustment reduces. The proposed hypothesis test is applied to two real data sets: the annual real GDP per capita of six European countries, and quarterly real GDP per capita of five European countries. The application results demonstrate that the proposed test displays reasonably good performance in classifying relatively short time series.

Impact and interest:

2 citations in Scopus
2 citations in Web of Science®
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ID Code: 73235
Item Type: Journal Article
Refereed: Yes
Keywords: Time series classification, Autoregressive models, Bias-adjusted AR estimators, Small samples, Hypothesis testing
DOI: 10.1016/j.csda.2012.11.014
ISSN: 0167-9473
Subjects: Australian and New Zealand Standard Research Classification > ECONOMICS (140000) > ECONOMETRICS (140300) > Time-Series Analysis (140305)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 Elsevier B.V.
Copyright Statement: NOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis, [Volume 60, (April 2013)] DOI: 10.1016/j.csda.2012.11.014
Deposited On: 02 Jul 2014 23:52
Last Modified: 21 Jun 2017 19:01

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