raw_missing_policy

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

Sub-layer

l1_c

Axis metadata

  • Default: 'preserve_raw_missing'

  • Sweepable: False

  • Status: operational

Operational status summary

  • Operational: 4 option(s)

  • Future: 0 option(s)

Options

preserve_raw_missing – operational

Pass raw NaN values through unchanged.

Default; raw missingness flows into L2.D imputation. Required for the McCracken-Ng EM-factor imputation workflow.

When to use

Default; required when L2.D will run EM-factor or similar global imputation.

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: zero_fill_leading_predictor_missing_before_tcode, impute_raw_predictors, drop_raw_missing_rows

Last reviewed 2026-05-05 by macroforecast author.

zero_fill_leading_predictor_missing_before_tcode – operational

Zero-fill leading predictor NaNs prior to t-code application.

Important for level-difference t-codes that fail when leading NaNs are interspersed with observed values. The zero-fill creates a clean prefix for differencing.

When to use

Tcode 1 / 2 / 5 / 6 pipelines where leading NaNs would propagate after differencing.

When NOT to use

When zero is a meaningful value for the predictor.

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: preserve_raw_missing

Last reviewed 2026-05-05 by macroforecast author.

impute_raw_predictors – operational

Impute raw predictor NaNs at L1 (before any L2 stage).

Runs a simple per-series imputation (mean / median / forward-fill) at L1. Useful when L2.D is disabled or when the user wants to pre-clean raw data before the t-code stage.

When to use

Pipelines that use no_transform t-codes and need cleaning at L1.

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: preserve_raw_missing, drop_raw_missing_rows

Last reviewed 2026-05-05 by macroforecast author.

drop_raw_missing_rows – operational

Drop rows containing any raw missing predictor.

Aggressive listwise deletion at the raw stage. Reduces panel size before any cleaning runs.

When to use

Sensitivity analyses; sanity checks against imputation effects.

When NOT to use

When the panel is small – you’ll lose a lot of 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: preserve_raw_missing

Last reviewed 2026-05-05 by macroforecast author.