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A note on in-sample and out-of-sample tests for Granger causality

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  • Shiu-Sheng Chen

    (Department of Economics, National Taiwan University, Taiwan)

Abstract
This paper studies in-sample and out-of-sample tests for Granger causality using Monte Carlo simulation. The results show that the out-of-sample tests may be more powerful than the in-sample tests when discrete structural breaks appear in time series data. Further, an empirical example investigating Taiwan's investment-saving relationship shows that Taiwan's domestic savings may be helpful in predicting domestic investments. It further illustrates that a possible Granger causal relationship is detected by out-of-sample tests while the in-sample test fails to reject the null of non-causality. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • Shiu-Sheng Chen, 2005. "A note on in-sample and out-of-sample tests for Granger causality," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 453-464.
    • Handle: RePEc:jof:jforec:v:24:y:2005:i:6:p:453-464
    DOI: 10.1002/for.960
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    9. Gary J. Cornwall & Jeffrey A. Mills & Beau A. Sauley & Huibin Weng, 2019. "Predictive Testing for Granger Causality via Posterior Simulation and Cross-validation," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A, volume 40, pages 275-292, Emerald Group Publishing Limited.
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