Forecast model category

Multivariate, mixed Frequency models

MIDAS Lasso
Mixed 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 Penalty
Mixed 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.
Unrestricted MIDAS
Unrestricted Mixed Data Sampling (MIDAS) models use high frequency indicators to predict a low frequency variable.
MIDAS
Mixed 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.

Explore more forecast models

Univariate, time series models
Multivariate, mixed Frequency models
Multivariate, hierarchical vector autoregression
Multivariate, group Lasso
Multivariate, penalized models
Multivariate, machine learning models
Multivariate, classic econometric models

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