midasml: 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) <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: 0.1.5
Depends: Matrix, R (≥ 3.5.0)
Imports: foreach, graphics, mcGlobaloptim, methods, lubridate, stats
Published: 2021-05-05
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
Materials: README
CRAN checks: midasml results

Downloads:

Reference manual: midasml.pdf
Package source: midasml_0.1.5.tar.gz
Windows binaries: r-devel: midasml_0.1.5.zip, r-release: midasml_0.1.5.zip, r-oldrel: midasml_0.1.4.zip
macOS binaries: r-release: midasml_0.1.5.tgz, r-oldrel: midasml_0.1.5.tgz
Old sources: midasml archive

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