dfm_mixed_mariano_murasawa – Mariano-Murasawa-style mixed-frequency dynamic factor model.#

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Operational op under axis family, sub-layer L4_A_model_selection, layer l4. Standalone callable: mf.functions.dfm_fit.

Function signature#

mf.functions.dfm_fit(
    X: np.ndarray | pd.DataFrame,
    y: np.ndarray | pd.Series,
) -> DFMFitResult

Parameters#

name

type

default

constraint

description

X

`np.ndarray

pd.DataFrame`

—

—

y

`np.ndarray

pd.Series`

—

—

Returns#

DFMFitResult — frozen dataclass with fit results.

Attribute

Type

Description

.n_factors

int

Number of dynamic factors.

.n_obs

int

Number of observations.

.predict(X)

np.ndarray

Predictions for new data X, shape (n_samples,).

.summary()

str

Table: factor count and observation count.

Behavior#

Linear-Gaussian state-space model with monthly-aggregator observation equation. Routes to statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQ when params.mixed_frequency = True and per-column frequency tags are supplied; otherwise falls back to the single-frequency DynamicFactor estimator (Kalman MLE).

When to use

Mixed-frequency nowcasting (e.g., quarterly GDP from monthly indicators).

In recipe context#

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

# Layer L4 recipe fragment
params:
  family: dfm_mixed_mariano_murasawa

References#

  • macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’

  • Mariano & Murasawa (2010) ‘A coincident index, common factors, and monthly real GDP’, Oxford Bulletin of Economics and Statistics 72(1).