Generalized cross-spectral test for nonlinear Granger causality with applications to money–output and price–volume relations

Haiqi Li, Wanling Zhong and Sung Y. Park

Economic Modelling, 2016, vol. 52, issue PB, 661-671

Abstract: In this study, we propose a test statistic based on a generalized cross-spectral distribution function to test for linear and nonlinear Granger causality. The test statistic considers all time series lags and, at the same time, avoids the “curse of dimensionality” problem. Moreover, it avoids having to choose a kernel function and bandwidth parameter. Since the generalized cross-spectral distribution test statistic asymptotically converges to a nonstandard distribution, we propose a wild bootstrap approach to approximate its critical values. A Monte Carlo simulation shows that the generalized cross-spectral distribution test statistic has better finite sample performance than Hong's (2001) test. In the empirical analysis, we perform empirical tests for Granger causality between U.S. money and output and between the return and volume of the CSI 300 Index and show that the proposed test statistic succeeds in capturing nonlinear Granger causality.

Keywords: Nonlinear Granger causality; Generalized cross-spectral distribution; Money–output relation; Return–volume relation (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:52:y:2016:i:pb:p:661-671

DOI: 10.1016/j.econmod.2015.09.037

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