midasml

midasml - Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series and Panel 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 ¹²³.

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

1. 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. ↩

2. Babii, A., Ghysels, E., & Striaukas, J. High-dimensional Granger causality tests with an application to VIX and news, (2022) Journal of Financial Econometrics, Forthcoming. ↩

3. Babii, A., R. Ball, Ghysels, E., & Striaukas, J. Machine learning panel data regressions with heavy-tailed dependent data: Theory and application, (2022) Journal of Econometrics, Forthcoming. ↩