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

Documentation for package ‘midasml’ version 0.0.5

midasml-package	Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data
apply_transform	Time series matrix transformation
beta_w	Beta density polynomial weights
dateMatch	Match dates
expalmon_w	Exponential Almon polynomial weights
gb	Gegenbauer polynomials shifted to [a,b]
lb	Legendre polynomials shifted to [a,b]
macro_midasml	GDP nowcasting using midasML approach example data
market_ret	SNP500 returns
midasml	Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data
midasml_forecast	MIDAS ML regression prediction function
midas_ardl	ARDL-MIDAS regression
midas_dl	DL-MIDAS regression
mixed_freq_data	MIDAS data structure
mixed_freq_data_mhorizon	MIDAS data structure
mixed_freq_data_single	MIDAS data structure
monthBegin	Beginning of the month date
monthEnd	End of the month date
panel_sgl	Panel sg-LASSO regression model
plot_weights	MIDAS weights plot function
predict.panel_sgl	Computes prediction for the sg-LASSO panel regression model
predict.reg_sgl	Computes prediction for the sg-LASSO linear regression
qtarget.sort_midasml	High-dimensional mixed frequency data sort function
rbeta_w	Restricted Beta density polynomial weights
reg_sgl	Linear sg-LASSO regression
sgl_fit	sg-LASSO regression
transform_dt	Time series vector transformation
us_rgdp	US real GDP data with several high-frequency predictors