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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: | 0.1.10 |
| Depends: | Matrix, R (≥ 3.5.0) |
| Imports: | doRNG, doParallel, foreach, graphics, randtoolbox, snow, methods, lubridate, stats |
| Published: | 2022-04-29 |
| Author: | 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) |
| 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 |
| Reference manual: | midasml.pdf |
| Package source: | midasml_0.1.10.tar.gz |
| Windows binaries: | r-devel: midasml_0.1.10.zip, r-release: midasml_0.1.10.zip, r-oldrel: midasml_0.1.10.zip |
| macOS binaries: | r-release (arm64): midasml_0.1.10.tgz, r-oldrel (arm64): midasml_0.1.10.tgz, r-release (x86_64): midasml_0.1.10.tgz, r-oldrel (x86_64): midasml_0.1.10.tgz |
| Old sources: | midasml archive |
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These binaries (installable software) and packages are in development.
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