Comparison of non-parametric and semi-parametric tests in detecting long memory

Mohamed Boutahar

Journal of Applied Statistics, 2009, vol. 36, issue 9, 945-972

Abstract: The first two stages in modelling times series are hypothesis testing and estimation. For long memory time series, the second stage was studied in the paper published in [M. Boutahar et al., Estimation methods of the long memory parameter: monte Carlo analysis and application, J. Appl. Statist. 34(3), pp. 261-301.] in which we have presented some estimation methods of the long memory parameter. The present paper is intended for the first stage, and hence completes the former, by exploring some tests for detecting long memory in time series. We consider two kinds of tests: the non-parametric class and the semi-parametric one. We precise the limiting distribution of the non-parametric tests under the null of short memory and we show that they are consistent against the alternative of long memory. We perform also some Monte Carlo simulations to analyse the size distortion and the power of all proposed tests. We conclude that for large sample size, the two classes are equivalent but for small sample size the non-parametric class is better than the semi-parametric one.

Keywords: hypothesis testing; long memory; power; short memory; size (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1080/02664760802562464

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