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 (sg-LASSO) estimator. For more information on the `midasml' approach see Babii, Ghysels, and Striaukas (2021, JBES forthcoming) . The package is equipped with the fast implementation of the sg-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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("midasml")

0.1.0 by Jonas Striaukas, 3 days ago


Report a bug at https://github.com/jstriaukas/midasml/issues


Browse source code at https://github.com/cran/midasml


Authors: Jonas Striaukas [cre, aut] , Andrii Babii [aut] , Eric Ghysels [aut] , Alex Kostrov [ctb] (Contributions to analytical gradients for non-linear low-dimensional MIDAS estimation code)


Documentation:   PDF Manual  


GPL (>= 2) license


Imports graphics, stats, mcGlobaloptim, methods, lubridate

Depends on Matrix


See at CRAN