DP3671 In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?

Author(s): Atsushi Inoue, Lutz Kilian
Publication Date: December 2002
Keyword(s): data mining, parameter instability, predictability test, reliability of inference
JEL(s): C12, C22, C52
Programme Areas: International Macroeconomics
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=3671

It is widely known that significant in-sample evidence of predictability does not guarantee significant out-of-sample predictability. This is often interpreted as an indication that in-sample evidence is likely to be spurious and should be discounted. In this Paper we question this conventional wisdom. Our analysis shows that neither data mining nor parameter instability is a plausible explanation of the observed tendency of in-sample tests to reject the no predictability null more often than out-of-sample tests. We provide an alternative explanation based on the higher power of in-sample tests of predictability. We conclude that results of in-sample tests of predictability will typically be more credible than results of out-of-sample tests.