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In-sample tests of predictability are superior to pseudo-out-of-sample tests, even when data mining

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  • Hunt, Ian

Abstract

This paper analyses straightforward Bonferroni adjustments to critical values of in-sample tests of predictability, when data mining is used to search across models. Unlike conventional pseudo-out-of-sample tests, these in-sample tests have stable family-wise error rates (FWERs) when searching for models that predict well. Furthermore, when data mining, these in-sample tests have more power than pseudo-out-of-sample tests for identifying true predictability.

Suggested Citation

  • Hunt, Ian, 2022. "In-sample tests of predictability are superior to pseudo-out-of-sample tests, even when data mining," International Journal of Forecasting, Elsevier, vol. 38(3), pages 872-877.
    • Handle: RePEc:eee:intfor:v:38:y:2022:i:3:p:872-877
    DOI: 10.1016/j.ijforecast.2021.05.006
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    References listed on IDEAS

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    1. Peter Reinhard Hansen & Allan Timmermann, 2015. "Equivalence Between Out‐of‐Sample Forecast Comparisons and Wald Statistics," Econometrica, Econometric Society, vol. 83, pages 2485-2505, November.
    2. Francis X. Diebold, 2015. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 1-1, January.
    3. Yoav Benjamini & Marina Bogomolov, 2014. "Selective inference on multiple families of hypotheses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 297-318, January.
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