Package: midasml 0.1.10

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.

Authors:Jonas Striaukas [cre, aut], Andrii Babii [aut], Eric Ghysels [aut], Alex Kostrov [ctb]

midasml_0.1.10.tar.gz
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midasml_0.1.10.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
midasml/json (API)

# Install 'midasml' in R:
install.packages('midasml', repos = c('https://jstriaukas.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jstriaukas/midasml/issues

Uses libs:
  • fortran– Runtime library for GNU Fortran applications
Datasets:

On CRAN:

Conda:

forecasting-modelsmachine-learningnowcasting-modelssparse-group-lassotime-seriesfortranglibc

4.89 score 44 stars 35 scripts 847 downloads 17 exports 16 dependencies

Last updated from:a2e547ce1e. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK139
linux-devel-x86_64OK133
source / vignettesOK195
linux-release-arm64OK141
linux-release-x86_64OK124
macos-release-arm64OK87
macos-release-x86_64OK197
macos-oldrel-arm64OK77
macos-oldrel-x86_64OK171
windows-develOK101
windows-releaseOK99
windows-oldrelOK102
wasm-releaseOK106

Exports:cv.panel.sglfitcv.sglfitdateMatchgbic.panel.sglfitic.sglfitlbmidas.ardlmixed_freq_datamixed_freq_data_singlemonthBeginmonthEndreg.panel.sglreg.sglsglfitthetafittscv.sglfit

Dependencies:codetoolscpp11digestdoParalleldoRNGforeachgenericsiteratorslatticelubridateMatrixrandtoolboxrngtoolsrngWELLsnowtimechange