`truncate_to_balanced` – Trim leading / trailing rows until every series is observed.#

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Operational op under axis frame_edge_policy, sub-layer l2_e, layer l2. Standalone callable: mf.functions.truncate_to_balanced_clean.

Function signature#

mf.functions.truncate_to_balanced_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#

Makes the panel rectangular by removing rows where any predictor (or the target, depending on scope) is missing. Standard for factor-model-style studies that need a balanced panel.

When to use

Default for high-dimensional studies; pairs with em_factor imputation for the interior.

In recipe context#

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

# Layer L2 recipe fragment
params:
  frame_edge_policy: truncate_to_balanced

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).

truncate_to_balanced – Trim leading / trailing rows until every series is observed.#