RTransferEntropy - Quantifying information flow between different time series using effective transfer entropy

被引：121

作者：

Behrendt, Simon ^{[1
]}

Dimpfl, Thomas ^{[2
]}

Peter, Franziska J. ^{[1
]}

Zimmermann, David J. ^{[3
]}

机构：

[1] Zeppelin Univ, Dept Empir Finance & Econometr, D-88045 Friedrichshafen, Germany

[2] Univ Tubingen, Fac Econ & Social Sci, Sch Business & Econ, Dept Stat Econometr & Empir Econ Res, D-72074 Tubingen, Germany

[3] Univ Witten Herdecke, Dept Banking & Finance, D-58448 Witten, Germany

来源：

SOFTWAREX | 2019年 / 10卷

关键词：

Shannon transfer entropy; Renyi transfer entropy; Effective transfer entropy; Bootstrap inference; R; MODEL;

D O I：

10.1016/j.softx.2019.100265

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

This paper shows how to quantify and test for the information flow between two time series with Shannon transfer entropy and Renyi transfer entropy using the R package RTransferEntropy. We discuss the methodology, the bias correction applied to calculate effective transfer entropy and outline how to conduct statistical inference. Furthermore, we describe the package in detail and demonstrate its functionality by means of several simulated processes and present an application to financial time series. (C) 2019 The Authors. Published by Elsevier B.V.

引用

页数：9

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