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 estimator. For more information on the midasml approach see [1, 2].
The package is equipped with the fast implementation of the sparse-group 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.
- Julia implmentation of the midasml method is available here.
# CRAN version - 0.1.5-1
install.packages("midasml")
# Development version - 0.1.5-1
# install.packages("devtools")
library(devtools)
install_github("jstriaukas/midasml")
[1] Babii, A., Ghysels, E., & Striaukas, J. (2021). Machine learning time series regressions with an application to nowcasting. forthcoming at Journal of Business & Economic Statistics, https://doi.org/10.1080/07350015.2021.1899933.
[2] Babii, A., Ball, R., Ghysels, E., & Striaukas, J. (2021). Machine learning panel data regressions with an application to nowcasting price-earnings ratios. https://arxiv.org/abs/2008.03600.