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In-sample or out-of-sample tests of predictability: which one should we use?

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  • Inoue, Atsushi
  • Kilian, Lutz

Abstract

It is widely known that significant in-sample evidence of predictability does not garantuee significant out-of-sample predictability. This is often interpreted as an indiciation 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-smaple 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. JEL Classification: C12, C22, C52

Suggested Citation

  • Inoue, Atsushi & Kilian, Lutz, 2002. "In-sample or out-of-sample tests of predictability: which one should we use?," Working Paper Series 195, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:2002195
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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