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Time series cross validation: A theoretical result and finite sample performance

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  • Deng, Ai

Abstract

We provide a theoretical result for the time series-based cross validation that sheds light on the choice of validation sample size. We also consider an alternative way to construct validation samples and demonstrate the improved performance in certain situations via simulations.

Suggested Citation

  • Deng, Ai, 2023. "Time series cross validation: A theoretical result and finite sample performance," Economics Letters, Elsevier, vol. 233(C).
    • Handle: RePEc:eee:ecolet:v:233:y:2023:i:c:s0165176523003944
    DOI: 10.1016/j.econlet.2023.111369
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    References listed on IDEAS

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    1. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
    2. Brownlees, Christian T. & Gallo, Giampiero M., 2011. "Shrinkage estimation of semiparametric multiplicative error models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 365-378, April.
    3. Racine, Jeff, 2000. "Consistent cross-validatory model-selection for dependent data: hv-block cross-validation," Journal of Econometrics, Elsevier, vol. 99(1), pages 39-61, November.
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