Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data

The 'midasml' package implements estimation and prediction methods for high-dimensional mixed-frequency (MIDAS) time-series and panel data regression models. The regularized MIDAS models are estimated using orthogonal (e.g. Legendre) polynomials and sparse-group LASSO (sg-LASSO) estimator. For more information on the `midasml' approach see Babii, Ghysels, and Striaukas (2021, JBES forthcoming) . The package is equipped with the fast implementation of the sg-LASSO estimator by means of proximal block coordinate descent. High-dimensional mixed frequency time-series data can also be easily manipulated with functions provided in the package.


Reference manual

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0.1.0 by Jonas Striaukas, 3 days ago

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Authors: Jonas Striaukas [cre, aut] , Andrii Babii [aut] , Eric Ghysels [aut] , Alex Kostrov [ctb] (Contributions to analytical gradients for non-linear low-dimensional MIDAS estimation code)

Documentation:   PDF Manual  

GPL (>= 2) license

Imports graphics, stats, mcGlobaloptim, methods, lubridate

Depends on Matrix

See at CRAN