refit_policy

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Axis refit_policy on sub-layer L4_C_training_window (layer l4).

Sub-layer

L4_C_training_window

Axis metadata

• Default: 'every_origin'

• Sweepable: True

• Status: operational

Operational status summary

• Operational: 3 option(s)

• Future: 0 option(s)

Options

every_origin – operational

Re-fit the model at every walk-forward origin.

Most expensive but most accurate – the model’s coefficients update with every new observation.

When to use

Default. Standard walk-forward protocol.

References

• macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’

Last reviewed 2026-05-04 by macroforecast author.

every_n_origins – operational

Re-fit every n origins (caps refit cost).

Requires leaf_config.refit_interval. Saves wall-clock when fits are slow but introduces stale-coefficient bias.

When to use

Long sweeps with slow estimators (e.g., LSTM / xgboost on large panels).

References

• macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’

Last reviewed 2026-05-04 by macroforecast author.

single_fit – operational

Fit once on the full sample; use the same coefficients at every origin.

Equivalent to in-sample evaluation. Useful for parameter-stability studies but does not test out-of-sample performance.

When to use

In-sample studies; coefficient-stability pins.

References

• macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’

Last reviewed 2026-05-04 by macroforecast author.