Estimation and Prediction Methods for High-Dimensional Mixed
Frequency Time Series Data
person("Jonas", "Striaukas", email = "jonas.striaukas@gmail.com", role = c("aut", "cre"))
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.

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.

c(
person("Jonas", "Striaukas",  role = c("cre","aut"), email =  "jonas.striaukas@gmail.com"),
person("Andrii", "Babii",  role = c("aut"),
email =  "andrii@email.unc.edu"),
person(c("Eric", "Ghysels"), role = c("aut"),
email =  "eghysels@unc.edu"),
person("Alex", "Kostrov",  role = c("ctb"),
comment = "Contributions to analytical gradients for non-linear low-dimensional MIDAS estimation code", email =  "alexander.kostrov@unisg.ch"))
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.
graphics, stats, mcGlobaloptim, methods, lubridate
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)