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A Note on Causality Tests

Zhidong Bai, Wing‐Keung Wong, Bing-Zhi Zhang

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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 (1994) develop a nonlinear Granger causality test in a bivariate setting to investigate the nonlinear causality between stock prices and trading volume. In this paper, we first discuss linear causality tests in multivariate settings and thereafter develop a non-linear causality test in multivariate settings.

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What this paper is about

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 (1994) develop a nonlinear Granger causality test in a bivariate setting to investigate the nonlinear causality between stock prices and trading volume. In this paper, we first discuss linear causality tests in multivariate settings and thereafter develop a non-linear causality test in multivariate settings.

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Available 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 (1994) develop a nonlinear Granger causality test in a bivariate setting to investigate the nonlinear causality between stock prices and trading volume. In this paper, we first discuss linear causality tests in multivariate settings and thereafter develop a non-linear causality test in multivariate settings.

Key concepts: Bivariate analysis, Multivariate statistics, Causality (physics), Granger causality, Econometrics, Multivariate analysis, Test (biology), Mathematics

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