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

机译:非参数和半参数测试在检测长记忆中的比较

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摘要

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 equivaient but for small sample size the non-parametric class is better than the semi-parametric one.
机译:建模时间序列的前两个阶段是假设检验和估计。对于较长的存储时间序列,[M。 Boutahar等人,长记忆参数的估计方法:蒙特卡洛分析和应用,J。统计员。 34(3),第261-301页],其中我们提出了一些长记忆参数的估算方法。本文旨在用于第一阶段,因此通过探索一些检测时间序列中长记忆的测试来完善前一阶段。我们考虑两种测试:非参数类和半参数类。我们在短存储为零的情况下精确化了非参数检验的极限分布,并证明了它们与长存储的选择是一致的。我们还执行了一些蒙特卡洛模拟,以分析尺寸失真和所有拟议测试的功效。我们得出结论,对于大样本量,这两个类别是等效的,但对于小样本量,非参数类别要优于半参数类别。

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