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

Abstract:     
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.

Number of Pages in PDF File: 43

Keywords: Predictability test, data mining, structural change, out-of-sample inference

JEL Classification: C12, C22, C52

working papers series