Forecast model category

Multivariate, hierarchical vector autoregression

Hierarchical Vector Auto Regression, HVAR models, alleviate the problem of forecast performance starting to degrade as each added variable is treated democratically despite more distant data generally tending to be less useful in forecasting. Instead of imposing a single, universal lag order, lags can vary across in HVAR models. There are no exogenous variables in the HVAR framework.

HVAR Elementwise Lasso

The most general structure, in each marginal model, each series may have its own maximum lag.

HVAR Own/Other Lasso

Imposes an additional layer of hierarchy: prioritizing “own” lags over “other” lags in the HVAR framework.

HVAR Componentwise Lasso

In Componentwise models all variables have the same maximum lag.

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