`em_factor` – EM-factor imputation (McCracken-Ng default).#

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Operational op under axis imputation_policy, sub-layer l2_d, layer l2. Standalone callable: mf.functions.em_factor_impute_clean.

Function signature#

mf.functions.em_factor_impute_clean(
    panel: pd.DataFrame,
) -> pd.DataFrame

Parameters#

name	type	default	constraint	description
`panel`	`pd.DataFrame`	—	—	Input panel. Each column is a variable; rows are time periods. Series is promoted to a single-column DataFrame internally.

Returns#

pd.DataFrame — scalar result.

Behavior#

Iterative EM algorithm: alternates between (1) fitting a factor model to the currently-imputed panel and (2) imputing missing cells from the factor model’s prediction. Converges to a low-rank fill consistent with the cross-series factor structure.

Used per-origin under imputation_temporal_rule = expanding_window_per_origin so the imputation respects the walk-forward information set.

When to use

Default for FRED-MD/QD high-dimensional panels.

In recipe context#

Set params.imputation_policy = "em_factor" in the relevant layer to activate this op within a recipe:

# Layer L2 recipe fragment
params:
  imputation_policy: em_factor

References#

macroforecast design Part 2, L2: ‘preprocessing is the only layer with a strict A→B→C→D→E execution order; every cell follows the same pipeline.’
Stock & Watson (2002) ‘Macroeconomic Forecasting Using Diffusion Indexes’, JBES 20(2).

em_factor – EM-factor imputation (McCracken-Ng default).#