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 sans restrictionLes modèles MIDAS (Unrestricted Mixed Data Sampling) utilisent des indicateurs à haute fréquence pour prédire une variable à basse fréquence.