A note on in-sample and out-of-sample tests for Granger causality
AbstractThis 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.
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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.
Volume (Year): 24 (2005)
Issue (Month): 6 ()
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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966
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- Christian Schulz, 2007. "Forecasting economic growth for Estonia : application of common factor methodologies," Bank of Estonia Working Papers 2007-09, Bank of Estonia, revised 04 Sep 2007.
- Hui Jun ZHANG & Jean-Marie DUFOUR & John W. GALBRAITH, 2013. "Exchange Rates and Commodity Prices : Measuring Causality at Multiple Horizons," Cahiers de recherche 14-2013, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
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