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The effect of round-off error on long memory processes. (English) Zbl 1329.62382

Summary: We study how the round-off (or discretization) error changes the statistical properties of a Gaussian long memory process. We show that the autocovariance and the spectral density of the discretized process are asymptotically rescaled by a factor smaller than one, and we compute exactly this scaling factor. Consequently, we find that the discretized process is also long memory with the same Hurst exponent as the original process. We consider the properties of two estimators of the Hurst exponent, namely the local Whittle (LW) estimator and the detrended fluctuation analysis (DFA). By using analytical considerations and numerical simulations we show that, in presence of round-off error, both estimators are severely negatively biased in finite samples. Under regularity conditions we prove that the LW estimator applied to discretized processes is consistent and asymptotically normal. Moreover, we compute the asymptotic properties of the DFA for a generic (i.e., non-Gaussian) long memory process and we apply the result to discretized processes.

MSC:

62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
60G15 Gaussian processes
62M15 Inference from stochastic processes and spectral analysis
62M09 Non-Markovian processes: estimation
65G50 Roundoff error

Software:

longmemo
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References:

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