0381822 20240103201341.220121030235959.9 000305302600006 10.1016/j.physa.2012.04.005 9 s. cav_un_epca*02574230378-437139117 (2012)4252-4260Elsevier Rescaled range analysis Modified rescaled range analysis Hurst exponent Long-term memory Short-term memory cav_un_auth*0256902 Krištoufek Ladislav Ekonometrie Department of Econometrics E E UTIA-B Department of Econometrics Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2012/E/kristoufek-how are rescaled range analyses affected by different memory and distributional properties.pdf 118310 GA UK CZ cav_un_auth*0274537 261 501 SVV CZ GA402/09/0965 GA ČR cav_un_auth*0253176 In this paper, we present the results of Monte Carlo simulations for two popular techniques of long-range correlation detection — classical and modified rescaled range analyses. A focus is put on an effect of different distributional properties on an ability of the methods to efficiently distinguish between short-term memory and long-term memory. To do so, we analyze the behavior of the estimators for independent, short-range dependent, and long-range dependent processes with innovations from eight different distributions. We find that apart from a combination of very high levels of kurtosis and skewness, both estimators are quite robust to distributional properties. Importantly, we show that R/S is biased upwards (yet not strongly) for short-range dependent processes, while M-R/S is strongly biased downwards for long-range dependent processes regardless of the distribution of innovations. 2013 AH 1 RVO:67985556 http://hdl.handle.net/11104/0212203 PHYSICSMULTIDISCIPLINARY 1.651 0.51 7.7 0.0279 0.475 15376 674 87 0.677 34.67 1.76 Q2 44.7 66.867 Q3 Q3 2012 000305302600006 WOS cav_un_epca*0257423 Physica. A : Statistical Mechanics and its Applications 0378-4371 1873-2119 Roč. 391 č. 17 2012 4252 4260 Elsevier