Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data

The 'midasml' estimation and prediction methods for high dimensional time series regression models under mixed data sampling data structures using structured-sparsity penalties and orthogonal polynomials. For more information on the 'midasml' approach see Babii, Ghysels, and Striaukas (2021, JBES forthcoming) . Functions that compute MIDAS data structures were inspired by MIDAS 'Matlab' toolbox (v2.3) written by Eric Ghysels.


Reference manual

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


0.0.6 by Jonas Striaukas, a month ago

Report a bug at

Browse source code at

Authors: Jonas Striaukas [aut, cre]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp, lubridate, parallel, doSNOW, stats, optimx, quantreg

Depends on foreach

Linking to Rcpp, RcppArmadillo

See at CRAN