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:	0.1.10
Depends:	Matrix, R (≥ 3.5.0)
Imports:	doRNG, doParallel, foreach, graphics, randtoolbox, snow, methods, lubridate, stats
Published:	2022-04-29
DOI:	10.32614/CRAN.package.midasml
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

Documentation:

Reference manual:

midasml.pdf

Downloads:

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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