`dfm` – Dynamic factor model – Kalman state-space factor extraction.#

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Operational op under axis op, sub-layer L3_A_step_op, layer l3. Standalone callable: mf.functions.dfm_transform.

Function signature#

mf.functions.dfm_transform(
    panel: pd.DataFrame,
    n_factors: int,
) -> 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.
`n_factors`	`int`	`3`	>= 1	Number of latent dynamic factors to extract. Clamped internally to min(T_clean, K) - 1.

Returns#

pd.DataFrame — scalar result.

Behavior#

statsmodels DynamicFactor MLE estimate of latent factors with idiosyncratic AR(1) errors. Differs from pca in that factors are smoothed via the Kalman filter and respect a factor-VAR transition.

When the panel is mixed-frequency (FRED-SD), the runtime auto-routes to DynamicFactorMQ (Mariano-Murasawa 2003).

When to use

Smoothed factors with an explicit dynamic; mixed-frequency panels (FRED-SD).

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: dfm

References#

macroforecast design Part 2, L3: ‘feature engineering is a DAG of typed transforms; cascade-depth bounds the longest chain at cascade_max_depth.’
Mariano & Murasawa (2003) ‘A new coincident index of business cycles based on monthly and quarterly series’, JAE 18(4): 427-443.

dfm – Dynamic factor model – Kalman state-space factor extraction.#