On Performance of some Methods of Detecting Nonlinear Stationary and Non-stationary Time Series Data
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Date
2016
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Publisher
Faculty of Physical Sciences, Federal University of Lafia, Nigeria.
Abstract
There has been growing interest in exploiting potential forecast gains from the nonlinear structure of autoregressive time series. Several models are available to fit nonlinear time series data. However, before investigating specific nonlinear models for time series data, it is desirable to have a test of nonlinearity in the data. And since most of real life data collected are non-stationary data. Statistical tests have been proposed in the literature to help analysts to check for the presence of nonlinearities in observed time series, these tests include Keenan and Tsay tests, and they have been used under the assumption that data is stationary. The effect of the stationarity and non-stationarity were studied on simulated data based on general class of linear and nonlinear autoregressive structure using R-software. The powers of the tests were compared at different sample sizes for the two cases. It was observed that the Tsay F-test performed better than Keenan’s tests with little order of autoregressive and increase in sample size when data is non-stationary and vice-versa when data is stationary. Finally, we provided illustrative examples by applying the statistical tests to real life datasets and results obtained were desirable.
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Keywords
Nonlinearity, Tsay’s F Test, Keenan’s Test, Stationarity, Non-stationarity
Citation
FULafia Journal of Science and Technology