`em_multivariate` – Multivariate-Gaussian EM imputation.#

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

Function signature#

mf.functions.em_multivariate_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#

Models the full panel as multivariate Gaussian and imputes missing cells via Schur-complement conditioning. More flexible than em_factor (no rank cap) but more expensive on large panels (O(p²) per iteration).

When to use

Smaller panels (≤ 50 series) where the full covariance is tractable.

In recipe context#

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

# Layer L2 recipe fragment
params:
  imputation_policy: em_multivariate

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.’

em_multivariate – Multivariate-Gaussian EM imputation.#