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

Daniel Peña, Ezequiel Smucler and Victor J. Yohai

International Journal of Forecasting, 2021, vol. 37, issue 4, 1498-1508

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.

Keywords: L1 penalization; Lasso; Principal components; Dynamic factor models; Cross validation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:37:y:2021:i:4:p:1498-1508

DOI: 10.1016/j.ijforecast.2020.10.008

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