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.
- Version0.1.10
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- Last release04/29/2022
Team
Jonas Striaukas
Andrii Babii
Show author detailsRolesAuthorEric Ghysels
Show author detailsRolesAuthorAlex Kostrov
Show author detailsRolesContributor
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- Depends1 package
- Imports6 packages