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Sparse estimation of dynamic principal components for forecasting high-dimensional time series

Author

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  • Peña, Daniel
  • Smucler, Ezequiel
  • Yohai, Victor J.

Abstract

We present the sparse estimation of one-sided dynamic principal components (ODPCs) to forecast high-dimensional time series. The forecast can be made directly with the ODPCs or by using them as estimates of the factors in a generalized dynamic factor model. It is shown that a large reduction in the number of parameters estimated for the ODPCs can be achieved without affecting their forecasting performance.

Suggested Citation

  • Peña, Daniel & Smucler, Ezequiel & Yohai, Victor J., 2021. "Sparse estimation of dynamic principal components for forecasting high-dimensional time series," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1498-1508.
    • Handle: RePEc:eee:intfor:v:37:y:2021:i:4:p:1498-1508
    DOI: 10.1016/j.ijforecast.2020.10.008
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    References listed on IDEAS

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    Cited by:

    1. Escribano, Alvaro & Peña, Daniel & Ruiz, Esther, 2021. "30 years of cointegration and dynamic factor models forecasting and its future with big data: Editorial," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1333-1337.
    2. Mihnea Constantinescu, 2023. "Sparse Warcasting," Working Papers 01/2023, National Bank of Ukraine.

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    More about this item

    Keywords

    L1 penalization; Lasso; Principal components; Dynamic factor models; Cross validation;
    All these keywords.

    JEL classification:

    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance

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