Multivariate linear and nonlinear causality tests

Zhidong Bai; Wing-Keung Wong; Bingzhi Zhang

doi:10.1016/j.matcom.2010.06.008

Multivariate linear and nonlinear causality tests

Zhidong Bai
, Wing-Keung Wong^*
, Bingzhi Zhang

^*Corresponding author for this work

Research output: Contribution to journal › Journal article › peer-review

85 Citations (Scopus)

159 Downloads (Pure)

Abstract

The traditional linear Granger test has been widely used to examine the linear causality among several time series in bivariate settings as well as multivariate settings. Hiemstra and Jones [19] develop a nonlinear Granger causality test in bivariate settings to investigate the nonlinear causality between stock prices and trading volume. This paper extends their work by developing a nonlinear causality test in multivariate settings.

Original language	English
Pages (from-to)	5-17
Number of pages	13
Journal	Mathematics and Computers in Simulation
Volume	81
Issue number	1
Early online date	13 Jul 2010
DOIs	https://doi.org/10.1016/j.matcom.2010.06.008
Publication status	Published - Sept 2010

User-Defined Keywords

Linear Granger causality
Nonlinear Granger causality
U-statistics

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10.1016/j.matcom.2010.06.008

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title = "Multivariate linear and nonlinear causality tests",

abstract = "The traditional linear Granger test has been widely used to examine the linear causality among several time series in bivariate settings as well as multivariate settings. Hiemstra and Jones [19] develop a nonlinear Granger causality test in bivariate settings to investigate the nonlinear causality between stock prices and trading volume. This paper extends their work by developing a nonlinear causality test in multivariate settings.",

keywords = "Linear Granger causality, Nonlinear Granger causality, U-statistics",

author = "Zhidong Bai and Wing-Keung Wong and Bingzhi Zhang",

note = "Funding Information: The First author would like to acknowledge NSF China Grant number 10871036 and the grant from the Risk Management Institute, National 282 University of Singapore. The second author would like to thank Professors Robert B. Miller and Howard E. Thompson for their continuous guidance and encouragement. This research is partially supported by Northeast Normal University, the National University of Singapore, Hong Kong Baptist University, and Columbia University. ",

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AU - Bai, Zhidong

AU - Wong, Wing-Keung

AU - Zhang, Bingzhi

N1 - Funding Information: The First author would like to acknowledge NSF China Grant number 10871036 and the grant from the Risk Management Institute, National 282 University of Singapore. The second author would like to thank Professors Robert B. Miller and Howard E. Thompson for their continuous guidance and encouragement. This research is partially supported by Northeast Normal University, the National University of Singapore, Hong Kong Baptist University, and Columbia University.

PY - 2010/9

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N2 - The traditional linear Granger test has been widely used to examine the linear causality among several time series in bivariate settings as well as multivariate settings. Hiemstra and Jones [19] develop a nonlinear Granger causality test in bivariate settings to investigate the nonlinear causality between stock prices and trading volume. This paper extends their work by developing a nonlinear causality test in multivariate settings.

AB - The traditional linear Granger test has been widely used to examine the linear causality among several time series in bivariate settings as well as multivariate settings. Hiemstra and Jones [19] develop a nonlinear Granger causality test in bivariate settings to investigate the nonlinear causality between stock prices and trading volume. This paper extends their work by developing a nonlinear causality test in multivariate settings.

KW - Linear Granger causality

KW - Nonlinear Granger causality

KW - U-statistics

UR - https://www.scopus.com/pages/publications/77956268974

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DO - 10.1016/j.matcom.2010.06.008

M3 - Journal article

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SP - 5

EP - 17

JO - Mathematics and Computers in Simulation

JF - Mathematics and Computers in Simulation

IS - 1

ER -

Multivariate linear and nonlinear causality tests

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