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

publication date

  • October 2021

start page

  • 1498

end page

  • 1508


  • 4


  • 37

International Standard Serial Number (ISSN)

  • 0169-2070

Electronic International Standard Serial Number (EISSN)

  • 1872-8200


  • 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.


  • Statistics


  • cross validation; dynamic factor models; l1 penalization; lasso; principal components