TVPVARResult._regressor_labels#

TVPVARResult._regressor_labels()[source]#

Per-equation regressor labels, in design order.

Return type:

tuple[str, ...]

Returns:

Deterministic labels first, then "{name}.L{lag}" lag by lag; equation i occupies state entries i * w .. (i + 1) * w - 1 in this order.

Example

>>> import numpy as np
>>> y = np.random.default_rng(0).standard_normal((120, 2))
>>> res = TVPVAR(y, order=2, training=30).fit(n_draws=12, n_burn=4, seed=0)
>>> res._regressor_labels()
('const', 'y1.L1', 'y2.L1', 'y1.L2', 'y2.L2')