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Prediction in Locally Stationary Time Series

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  • Holger Dette
  • Weichi Wu

Abstract

We develop an estimator for the high-dimensional covariance matrix of a locally stationary process with a smoothly varying trend and use this statistic to derive consistent predictors in nonstationary time series. In contrast to the currently available methods for this problem the predictor developed here does not rely on fitting an autoregressive model and does not require a vanishing trend. The finite sample properties of the new methodology are illustrated by means of a simulation study and a financial indices study. Supplementary materials for this article are available online.

Suggested Citation

  • Holger Dette & Weichi Wu, 2022. "Prediction in Locally Stationary Time Series," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 370-381, January.
    • Handle: RePEc:taf:jnlbes:v:40:y:2022:i:1:p:370-381
    DOI: 10.1080/07350015.2020.1819296
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    Cited by:

    1. Jozef Barunik & Lukas Vacha, 2023. "The Dynamic Persistence of Economic Shocks," Papers 2306.01511, arXiv.org.

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