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

## Documentation for package ‘midasml’ version 0.0.5

- DESCRIPTION file.

## Help Pages

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