`var` – Vector autoregression VAR(p).#

Back to family axis | Back to L4 | Browse all options

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

Function signature#

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

Parameters#

name	type	default	constraint	description
`X`	`np.ndarray	pd.DataFrame`	—	—
`y`	`np.ndarray	pd.Series`	—	—

Returns#

VARFitResult — frozen dataclass with fit results.

Attribute	Type	Description
`.n_lags`	`int`	VAR lag order p.
`.n_obs`	`int`	Number of observations.
`.predict(X)`	`np.ndarray`	Predictions for new data X, shape (n_samples,).
`.summary()`	`str`	Table: lag order and observation count.

Behavior#

Joint AR(p) over the target plus its predictors. Uses statsmodels’ VAR and forecasts the target component of the joint system. Captures cross-series dynamics that single-equation AR misses.

When to use

Multi-series joint forecasting; impulse-response decomposition (paired with L7 orthogonalised_irf for Cholesky-identified shocks; generalized_irf reserved for the future Pesaran-Shin 1998 order-invariant variant).

When NOT to use

High-dimensional panels (VAR scales O(p²)); use BVAR shrinkage instead.

In recipe context#

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

# Layer L4 recipe fragment
params:
  family: var

References#

macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’
Sims (1980) ‘Macroeconomics and Reality’, Econometrica 48(1).

var – Vector autoregression VAR(p).#