forecast_strategy

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Axis forecast_strategy on sub-layer L4_B_forecast_strategy (layer l4).

Sub-layer

L4_B_forecast_strategy

Axis metadata

  • Default: 'direct'

  • Sweepable: True

  • Status: operational

Operational status summary

  • Operational: 3 option(s)

  • Future: 0 option(s)

Options

direct – operational

One model per horizon (h=1, h=6, h=12, …).

Fits a separate model for each horizon h, using y_{t+h} as the target. The standard horse-race protocol: simple to implement, no error compounding, more compute.

When to use

Default for most studies. Comparable across publications.

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.

iterated – operational

Fit h=1 model; apply recursively for h>1.

Trains a single model on (y_t, X_t) → y_{t+1}, then iterates the prediction h times. Faster (one fit per cell) but errors compound.

When to use

Speed-sensitive sweeps; replication of papers using iterated VAR.

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.

path_average – operational

Forecast the cumulative-average target over horizon h.

Pairs with the L3 cumulative_average target-construction op. Useful for studies forecasting the average growth rate over horizon h rather than the level.

When to use

Cumulative-growth forecasting (e.g., Stock-Watson 2002).

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.