Package: midasml 0.1.10

midasml: 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) <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.

Authors:Jonas Striaukas [cre, aut], Andrii Babii [aut], Eric Ghysels [aut], Alex Kostrov [ctb]

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Bug tracker:https://github.com/jstriaukas/midasml/issues

On CRAN:

forecasting-modelsmachine-learningnowcasting-modelssparse-group-lassotime-series

17 exports 38 stars 2.75 score 16 dependencies 4 scripts 1.5k downloads

Last updated 2 years agofrom:a2e547ce1e. Checks:OK: 9. Indexed: yes.

Exports:cv.panel.sglfitcv.sglfitdateMatchgbic.panel.sglfitic.sglfitlbmidas.ardlmixed_freq_datamixed_freq_data_singlemonthBeginmonthEndreg.panel.sglreg.sglsglfitthetafittscv.sglfit

Dependencies:codetoolscpp11digestdoParalleldoRNGforeachgenericsiteratorslatticelubridateMatrixrandtoolboxrngtoolsrngWELLsnowtimechange