TVPVARResult._width#

TVPVARResult._width[source]#

Regressors per equation, w = n_det + k p; the state has k w entries.

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._width, res.beta_mean.shape[1] == res.k_endog * res._width
(5, True)
On this page