Sparse estimation of dynamic principal components for forecasting high-dimensional time series |
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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. |
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Keywords: | L1 penalization Lasso Principal components Dynamic factor models Cross validation |
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