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

The 'midasml' estimation and prediction methods for high dimensional time series regression models under mixed data sampling data structures using structured-sparsity penalties and orthogonal polynomials. For more information on the 'midasml' approach see Babii, Ghysels, and Striaukas (2021, JBES forthcoming) <doi:10.1080/07350015.2021.1899933>. Functions that compute MIDAS data structures were inspired by MIDAS 'Matlab' toolbox (v2.3) written by Eric Ghysels.

Version: 0.0.6
Depends: foreach (≥ 1.4.4)
Imports: Rcpp (≥ 1.0.3), lubridate (≥ 1.7.4), parallel (≥ 3.5.2), doSNOW (≥ 1.0.18), stats (≥ 3.5.2), optimx (≥ 2020-4.2), quantreg (≥ 5.34)
LinkingTo: Rcpp, RcppArmadillo
Published: 2021-03-12
Author: Jonas Striaukas [aut, cre]
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

Downloads:

Reference manual: midasml.pdf
Package source: midasml_0.0.6.tar.gz
Windows binaries: r-devel: midasml_0.0.6.zip, r-release: midasml_0.0.6.zip, r-oldrel: midasml_0.0.6.zip
macOS binaries: r-release: midasml_0.0.6.tgz, r-oldrel: midasml_0.0.6.tgz
Old sources: midasml archive

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