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

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Author Info
Inoue, Atsushi
Kilian, Lutz

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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.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 3671.

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Date of creation: Dec 2002
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Handle: RePEc:cpr:ceprdp:3671

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Related research
Keywords: data mining parameter instability predictability test reliability of inference

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Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing

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