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midasml  

Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data
View on CRAN: Click here


Download and install midasml package within the R console
Install from CRAN:
install.packages("midasml")

Install from Github:
library("remotes")
install_github("cran/midasml")

Install by package version:
library("remotes")
install_version("midasml", "0.1.11")



Attach the package and use:
library("midasml")
Maintained by
Jonas Striaukas
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-06-25
Latest Update: 2022-04-29
Description:
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) . 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.
How to cite:
Jonas Striaukas (2020). midasml: Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data. R package version 0.1.11, https://cran.r-project.org/web/packages/midasml. Accessed 15 Jul. 2026.
Previous versions and publish date:
(2026-07-09 06:30), 0.0.1 (2020-06-25 18:10), 0.0.2 (2020-07-01 14:00), 0.0.3 (2020-07-03 18:40), 0.0.4 (2020-07-05 00:50), 0.0.5 (2020-07-05 23:20), 0.0.6 (2021-03-13 00:00), 0.1.0 (2021-04-14 14:00), 0.1.2 (2021-04-22 02:10), 0.1.3 (2021-04-22 16:00), 0.1.4 (2021-04-23 17:00), 0.1.5-1 (2021-05-20 10:30), 0.1.5 (2021-05-05 16:40), 0.1.6 (2021-11-30 15:30), 0.1.7 (2021-12-08 14:00), 0.1.8 (2021-12-16 10:00), 0.1.9-1 (2022-02-15 20:20), 0.1.9 (2022-01-10 14:22), 0.1.10 (2022-04-29 09:00)
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