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.
| Version: |
0.1.10 |
| Depends: |
Matrix, R (≥ 3.5.0) |
| Imports: |
doRNG, doParallel, foreach, graphics, randtoolbox, snow, methods, lubridate, stats |
| Published: |
2022-04-29 |
| Author: |
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) |
| Maintainer: |
Jonas Striaukas <jonas.striaukas at gmail.com> |
| BugReports: |
https://github.com/jstriaukas/midasml/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| CRAN checks: |
midasml results |
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