`mccracken_ng_iqr` – McCracken-Ng’s published IQR-multiple outlier rule.#

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Operational op under axis outlier_policy, sub-layer l2_c, layer l2. Standalone callable: mf.functions.iqr_outlier_clean.

Function signature#

mf.functions.iqr_outlier_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#

Flags any observation more than leaf_config.outlier_iqr_threshold (default 10) IQRs from the per-series median. The 10×IQR threshold is the published McCracken-Ng default and matches their replication scripts.

Pairs with an L2.C outlier_action to specify what happens to flagged observations (replace with NaN by default, then L2.D imputation fills them).

When to use

Default for FRED-based studies. Canonical replication path.

In recipe context#

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

# Layer L2 recipe fragment
params:
  outlier_policy: mccracken_ng_iqr

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.’
McCracken & Ng (2016) ‘FRED-MD: A Monthly Database for Macroeconomic Research’, JBES 34(4). (doi:10.1080/07350015.2015.1086655)

mccracken_ng_iqr – McCracken-Ng’s published IQR-multiple outlier rule.#