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 (2020) <arXiv:2005.14057>. Functions that compute MIDAS data structures were inspired by MIDAS 'Matlab' toolbox (v2.3) written by Eric Ghysels.

Version: 0.0.5
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: 2020-07-05
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
Materials: README
CRAN checks: midasml results


Reference manual: midasml.pdf
Package source: midasml_0.0.5.tar.gz
Windows binaries: r-devel: midasml_0.0.5.zip, r-release: midasml_0.0.5.zip, r-oldrel: midasml_0.0.5.zip
macOS binaries: r-release: midasml_0.0.5.tgz, r-oldrel: midasml_0.0.5.tgz
Old sources: midasml archive


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