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.
Imposes an additional layer of hierarchy: prioritizing “own” lags over “other” lags in the HVAR…
Read moreHVAR Elementwise LassoThe most general structure, in each marginal model, each series may have its own maximum…
Read moreHVAR Componentwise LassoIn Componentwise models all variables have the same maximum…
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Book a demoCross-temporal hierarchies, reconciliation weights and the research behind them.
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