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

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...

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Bibliographic Details
Published inInternational journal of forecasting Vol. 37; no. 4; pp. 1498 - 1508
Main Authors Peña, Daniel, Smucler, Ezequiel, Yohai, Victor J.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.10.2021
Subjects
Cross validation
Dynamic factor models
L1 penalization
Lasso
Principal components
Dynamic factor models
L1 penalization
Cross validation
Lasso
Principal components
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Summary: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.
ISSN:0169-2070
1872-8200
DOI:10.1016/j.ijforecast.2020.10.008