Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data
Cross-validation fit for panel sg-LASSO
Sorts cross-validation output for panel data regressions
Cross-validation fit for sg-LASSO
Sorts cross-validation output
Identify data frequency
Transform date vector to numeric matrix
Match dates
Computes the difference between two dates.
Gegenbauer polynomials shifted to [a,b]
Information criteria fit for panel sg-LASSO
Compute the penalty based on chosen information criteria
Information criteria fit for sg-LASSO
Compute the number of lags
Legendre polynomials shifted to [a,b]
MIDAS regression
midasml
MIDAS data structure
MIDAS data structure
Compute mode of a vector
Beginning of the month date
End of the month date
Computes prediction
Computes prediction
Computes prediction
Computes prediction
Computes prediction
Regression fit for panel sg-LASSO
Fit for sg-LASSO regression
Fits sg-LASSO regression
Nodewise LASSO regressions to fit the precision matrix
Time series cross-validation fit for sg-LASSO
Sorts time series cross-validation output
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.