On estimating the intensity of long-range dependence in finite and infinite variance time series
A practical guide to heavy tails
Bootstrapping long memory tests: Some Monte Carlo results
Computational Statistics & Data Analysis
Tests of Random Walk: A Comparison of Bootstrap Approaches
Computational Economics
Generating LRD traffic traces using bootstrapping
ITC20'07 Proceedings of the 20th international teletraffic conference on Managing traffic performance in converged networks
Original article: Voter interacting systems applied to Chinese stock markets
Mathematics and Computers in Simulation
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Many time series in diverse fields have been found to exhibit long memory. This paper analyzes the behaviour of some of the most used tests of long memory: the R/S analysis, the modified R/S, the Geweke and Porter-Hudak (GPH) test and the detrended fluctuation analysis (DFA). Some of these tests exhibit size distortions in small samples. It is well known that the bootstrap procedure may correct this fact. Here I examine the size and power of those tests for finite samples and different distributions, such as the normal, uniform, and lognormal. In the short-memory processes such as AR, MA and ARCH and long memory ones such as ARFIMA, p-values are calculated using the post-blackening moving-block bootstrap. The Monte Carlo study suggests that the bootstrap critical values perform better. The results are applied to financial return time series.