Forecast model category

Multivariate, mixed frequency models

Mixed frequency forecasting models use higher frequency data to predict outcomes at a lower frequency and are commonly applied in nowcasting.
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.
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.

Explore more model categories

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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