midasml: Estimation and Prediction Methods for High-Dimensional MixedFrequency 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) <doi:10.1080/07350015.2021.1899933>. 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.
| Version: | 0.1.11 |
| Depends: | Matrix, R (≥ 3.5.0) |
| Imports: | doRNG,doParallel,foreach, graphics,randtoolbox,snow, methods,lubridate, stats |
| Published: | 2025-10-09 |
| DOI: | 10.32614/CRAN.package.midasml |
| Author: | Jonas Striaukas [cre, aut], Andrii Babii [aut], Jad Beyhum [aut], Eric Ghysels [aut], Alex Kostrov [ctb] (Contributions to analytical gradients for non-linear low-dimensional MIDAS estimation code) |
| Maintainer: | Jonas Striaukas <jonas.striaukas at gmail.com> |
| BugReports: | https://github.com/jstriaukas/midasml/issues |
| License: | GPL-2 |GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | midasml results |
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