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) <doi:10.1080/07350015.2021.1899933>. 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.
doRNG, doParallel, foreach, graphics, randtoolbox, snow,
methods, lubridate, stats

foreach, graphics, mcGlobaloptim, methods, lubridate, stats

doRNG, doParallel, foreach, graphics, mcGlobaloptim, methods,
lubridate, stats

Econometric methods for mixed frequency time series data analysis
Virmantas Kvedaras <virmantas.kvedaras@mif.vu.lt>, Vaidotas Zemlys
<vaidotas.zemlys@mif.vu.lt>
'simulate.R' 'midasr-package.R' 'midasreg.R' 'deriv.R'
'midas_r_methods.R' 'lagspec.R' 'imidasreg.R' 'midaslag.R'
'tests.R' 'modsel.R'

Methods and tools for mixed frequency time series data analysis. Allows estimation, model selection and forecasting for MIDAS regressions.

MASS, numDeriv, Matrix, forecast, stats, graphics, utils

Methods and tools for mixed frequency time series data analysis.
Allows estimation, model selection and forecasting for MIDAS regressions.

Virmantas Kvedaras <virmantas.kvedaras@ec.europa.eu>, Vaidotas Zemlys-Balevičius
<vaidotas.zemlys@mif.vu.lt>
R (>= 2.11.0), sandwich, optimx, quantreg
MASS, numDeriv, Matrix, forecast, zoo, stats, graphics, utils,
Formula, texreg, methods
'deriv.R' 'imidasreg.R' 'lagspec.R' 'midas_nlpr.R'
'midas_r_methods.R' 'midas_nlpr_methods.R' 'midas_qr_methods.R'
'midas_sp.R' 'midas_sp_methods.R' 'midaslag.R' 'midasqr.R'
'midasr-package.R' 'midasreg.R' 'modsel.R' 'nonparametric.R'
'simulate.R' 'tests.R'