midasml
midasml - Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data
About
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 12[^3].
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.
Software in other languages
- Julia implmentation of the midasml method is available here.
- MATLAB implmentation of the midasml method is available here.
- Python implmentation of the midasml method is being developed at here.
Run to install the package
# CRAN version - 0.1.10
install.packages("midasml")
# Development version - 0.1.10
# install.packages("devtools")
library(devtools)
install_github("jstriaukas/midasml")
Acknowledgements
Jonas Striaukas acknowledges that this material is based upon work supported by the Fund for Scientific Research-FNRS (Belgian National Fund for Scientific Research) under Grant #FC21388.
References
Footnotes
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Babii, A., Ghysels, E., & Striaukas, J. Machine learning time series regressions with an application to nowcasting, (2022) Journal of Business & Economic Statistics, Volume 40, Issue 3, 1094-1106. https://doi.org/10.1080/07350015.2021.1899933.
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Babii, A., Ghysels, E., & Striaukas, J. High-dimensional Granger causality tests with an application to VIX and news, (2022) Journal of Financial Econometrics, Forthcoming.
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