Partial unit root and surplus-lag Granger causality testing: A Monte Carlo simulation study

Lingxiang Zhang

doi:10.1080/03610926.2017.1295077

Partial unit root and surplus-lag Granger causality testing: A Monte Carlo simulation study

Lingxiang Zhang^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

Previous literature has shown that the addition of an untested surplus-lag Granger causality test can provide highly robust to stationary, non stationary, long memory, and structural break processes in the forcing variables. This study extends this approach to the partial unit root framework by simulation. Results show good size and power. Therefore, the surplus-lag approach is also robust to partial unit root processes.

Original language	English
Pages (from-to)	12317-12323
Number of pages	7
Journal	Communications in Statistics - Theory and Methods
Volume	46
Issue number	24
DOIs	https://doi.org/10.1080/03610926.2017.1295077
Publication status	Published - 17 Dec 2017

Keywords

Granger causality
Monte Carlo simulation
Partial unit root

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Zhang, L. (2017). Partial unit root and surplus-lag Granger causality testing: A Monte Carlo simulation study. Communications in Statistics - Theory and Methods, 46(24), 12317-12323. https://doi.org/10.1080/03610926.2017.1295077

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Partial unit root and surplus-lag Granger causality testing: A Monte Carlo simulation study

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Keywords

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