Package: midasml 0.1.10

Jonas Striaukas

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
midasml_0.1.10.tar.gz(r-4.5-noble)midasml_0.1.10.tar.gz(r-4.4-noble)
midasml_0.1.10.tgz(r-4.4-emscripten)midasml_0.1.10.tgz(r-4.3-emscripten)
midasml.pdf |midasml.html✨
midasml/json (API)

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

Bug tracker:https://github.com/jstriaukas/midasml/issues3 issues

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

On CRAN:

Conda:

fortran

1.60 score 4 stars 988 downloads 17 exports 16 dependencies

Last updated 3 years agofrom:5580798564. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 21 2025
R-4.5-linux-x86_64OKMar 21 2025
R-4.4-linux-x86_64OKMar 21 2025

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

Dependencies:codetoolscpp11digestdoParalleldoRNGforeachgenericsiteratorslatticelubridateMatrixrandtoolboxrngtoolsrngWELLsnowtimechange

Citation

To cite package ‘midasml’ in publications use:

Striaukas J, Babii A, Eric Ghysels (2022). midasml: Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data. R package version 0.1.10, https://CRAN.R-project.org/package=midasml.

Corresponding BibTeX entry: 

  @Manual{,
    title = {midasml: Estimation and Prediction Methods for
      High-Dimensional Mixed Frequency Time Series Data},
    author = {Jonas Striaukas and Andrii Babii and {Eric Ghysels}},
    year = {2022},
    note = {R package version 0.1.10},
    url = {https://CRAN.R-project.org/package=midasml},
  }