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

Shen Liu and Elizabeth Maharaj ()

Computational Statistics & Data Analysis, 2013, vol. 60, issue C, 32-49

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.

Keywords: Time series classification; Autoregressive models; Bias-adjusted AR estimators; Small samples; Hypothesis testing (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:60:y:2013:i:c:p:32-49

DOI: 10.1016/j.csda.2012.11.014

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