Počet záznamů: 1  

Fractal approach towards power-law coherency to measure cross-correlations between time series

  1. 1.
    SYSNO ASEP0473066
    Druh ASEPJ - Článek v odborném periodiku
    Zařazení RIVJ - Článek v odborném periodiku
    Poddruh JČlánek ve WOS
    NázevFractal approach towards power-law coherency to measure cross-correlations between time series
    Tvůrce(i) Krištoufek, Ladislav (UTIA-B) RID
    Celkový počet autorů1
    Zdroj.dok.Communications in Nonlinear Science and Numerical Simulation. - : Elsevier - ISSN 1007-5704
    Roč. 50, č. 1 (2017), s. 193-200
    Poč.str.8 s.
    Forma vydáníTištěná - P
    Jazyk dok.eng - angličtina
    Země vyd.NL - Nizozemsko
    Klíč. slovapower-law coherency ; power-law cross-correlations ; correlations
    Vědní obor RIVAH - Ekonomie
    Obor OECDApplied Economics, Econometrics
    CEPGP14-11402P GA ČR - Grantová agentura ČR
    Institucionální podporaUTIA-B - RVO:67985556
    UT WOS000399513200015
    EID SCOPUS85014923760
    DOI10.1016/j.cnsns.2017.02.018
    AnotaceWe focus on power-law coherency as an alternative approach towards studying power law cross-correlations between simultaneously recorded time series. To be able to study empirical data, we introduce three estimators of the power-law coherency parameter Hp based on popular techniques usually utilized for studying power-law cross-correlations detrended cross-correlation analysis (DCCA), detrending moving-average cross-correlation analysis (DMCA) and height cross-correlation analysis (HXA). In the finite sample properties study, we focus on the bias, variance and mean squared error of the estimators. We find that the DMCA-based method is the safest choice among the three. The HXA method is reasonable for long time series with at least 104 observations, which can be easily attainable in some disciplines but problematic in others. The DCCA-based method does not provide favorable properties which even deteriorate with an increasing time series length. The paper opens a new venue towards studying cross-correlations between time series.
    PracovištěÚstav teorie informace a automatizace
    KontaktMarkéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.
    Rok sběru2018