MIDASMixed Data Sampling (MIDAS) models use high frequency indicators to predict a low frequency variable. By fitting a lag distribution function the number of parameters is kept low, reducing the risk of over-fitting.
MIDAS LassoMixed Data Sampling (MIDAS) models use high frequency indicators to predict a low frequency variable. By applying a lasso penalty function the parameters are shrunk towards zero, reducing the risk of over-fitting.
MIDAS Sparse Group PenaltyMixed Data Sampling (MIDAS) models use high frequency indicators to predict a low frequency variable. By applying a sparse group penalty function the parameters are shrunk towards zero, reducing the risk of over-fitting.
MIDAS sin restriccionesLos modelos de muestreo mixto de datos sin restricciones (MIDAS) utilizan indicadores de alta frecuencia para predecir una variable de baja frecuencia.