missing_availability

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Axis missing_availability on sub-layer l1_c (layer l1).

Sub-layer

l1_c

Axis metadata

  • Default: 'zero_fill_leading_predictor_gaps'

  • Sweepable: False

  • Status: operational

Operational status summary

  • Operational: 4 option(s)

  • Future: 0 option(s)

Options

require_complete_rows – operational

Drop any row containing a missing value.

Strict listwise-deletion rule applied at L1 before L2 imputation. Useful when the recipe author prefers to lose rows rather than rely on imputation; produces a smaller, fully-observed panel.

When to use

Studies where imputation is methodologically inappropriate; sensitivity analyses against imputation effects.

When NOT to use

When the panel is sparsely observed – listwise deletion can leave too few rows.

References

  • macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’

Related options: keep_available_rows, impute_predictors_only

Last reviewed 2026-05-05 by macroforecast author.

keep_available_rows – operational

Keep every row that has the target observed.

Default; passes interior predictor NaNs through to L2.D for imputation. Ensures the maximum sample size while letting downstream imputation handle holes.

When to use

Default for FRED-MD / -QD recipes where L2.D EM imputation is the canonical workflow.

References

  • macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’

Related options: require_complete_rows, impute_predictors_only

Last reviewed 2026-05-05 by macroforecast author.

impute_predictors_only – operational

Impute predictor missings at L1; never impute the target.

Restricts imputation to the predictor block at L1 stage and forbids any target imputation in subsequent layers. Avoids accidentally back-filling the target via L2.D.

When to use

Recipes where the target should be the ground-truth signal and never imputed.

References

  • macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’

Related options: keep_available_rows

Last reviewed 2026-05-05 by macroforecast author.

zero_fill_leading_predictor_gaps – operational

Zero-fill leading predictor NaNs; preserve interior gaps.

Replaces leading NaNs (before the predictor’s first observation) with zero so the panel has a uniform start date. Interior NaNs pass through to L2.D unchanged.

When to use

FRED-SD panels where some series start later but the user wants a balanced start date.

When NOT to use

When zero is a meaningful value for the predictor – choose preserve_raw_missing instead.

References

  • macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’

Related options: require_complete_rows

Last reviewed 2026-05-05 by macroforecast author.