Authors

• Jonas Striaukas - jstriaukas
• Haris Karagiannakis - ckarag

About

The package aim is to provide Matlab users the implementation of midasml approach developed by ¹²³. The package is equipped with the fast implementation of the sparse-group LASSO estimator by means of proximal block coordinate descent, which is implemented in Fortran. User are advised to compile the Fortran code in the src folder on their system and compiler.

Software in other languages

• R implementation of the midasml method is available here.
• Julia implmentation of the midasml method is available here.
• Python implmentation of the midasml method is being developed at here.

Run to install the package

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. ↩