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

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Author Info
Shiu-Sheng Chen (Department of Economics, National Taiwan University, Taiwan)

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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.

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File URL: http://hdl.handle.net/10.1002/for.960
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Publisher Info
Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 24 (2005)
Issue (Month): 6 ()
Pages: 453-464
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Handle: RePEc:jof:jforec:v:24:y:2005:i:6:p:453-464

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  1. 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. [Downloadable!]
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