midasmlpy

Python implementation of the midasml approach - providing estimation and prediction methods for high-dimensional mixed-frequency time-series data

Authors

• Jonas Striaukas - jstriaukas
• Kris Stern - slim-patchy

About

The midasmlpy package implements estimation and prediction methods for high-dimensional mixed-frequency (MIDAS) time-series and panel data in regression models. The regularized MIDAS models are estimated using orthogonal (e.g. Legendre) polynomials and the sparse-group LASSO estimator. For more information on the midasmlpy approach there are references in the footnotes¹.

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

• A Julia implementation of the midasml method is available here.
• A MATLAB implementation of the midasml method is available here.
• An R implementation of the midasml method is available here.

Details on the Fortran code

The main subroutines are written in Fortran 90. Using Python 3.8.x, the code is compiled for several OS:

• sglfitF.cpython-38-x86_64-linux-gnu.so - compiled for Linux. Version: 20.04.4. Compiled with f2py3 and gfortran compiler.
• sglfitF.cp38-win_amd64.pyd - compiled for Windows. Version: Windows 10. Compiled with f2py and gfortran compiler.
• sglfitF.cpython-38-darwin.so - complied for macOS. Version: Monterey 12.3.1. Compiled with numpy.f2py and gfortran compiler.

In case you are running the code on a different platform, you can compile the Fortran code sglfitF.f90 by using f2py which is part of numpy.

Footnotes

1. Babii, A., Ghysels, E., & Striaukas, J. (2021). Machine learning time series regressions with an application to nowcasting. Journal of Business & Economic Statistics, 1-23. Now available at https://doi.org/10.1080/07350015.2021.1899933. ↩