Package: midasml 0.1.10
Jonas Striaukas
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:
midasml_0.1.10.tar.gz
midasml_0.1.10.tar.gz(r-4.5-noble)midasml_0.1.10.tar.gz(r-4.4-noble)
midasml_0.1.10.tgz(r-4.4-emscripten)midasml_0.1.10.tgz(r-4.3-emscripten)
midasml.pdf |midasml.html✨
midasml/json (API)
| # Install 'midasml' in R: |
| install.packages('midasml', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jstriaukas/midasml/issues
- alfred_vintages - ALFRED monthly and quarterly series vintages
- market_ret - SNP500 returns
- rgdp_dates - Real GDP release dates
- rgdp_vintages - Real GDP vintages
- us_rgdp - US real GDP data with several high-frequency predictors
Last updated 2 years agofrom:5580798564. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 22 2024 |
| R-4.5-linux-x86_64 | OK | Oct 22 2024 |
Exports:cv.panel.sglfitcv.sglfitdateMatchgbic.panel.sglfitic.sglfitlbmidas.ardlmixed_freq_datamixed_freq_data_singlemonthBeginmonthEndreg.panel.sglreg.sglsglfitthetafittscv.sglfit
Dependencies:codetoolscpp11digestdoParalleldoRNGforeachgenericsiteratorslatticelubridateMatrixrandtoolboxrngtoolsrngWELLsnowtimechange
