SignRestrictedSVARResult.acceptance_rate

property SignRestrictedSVARResult.acceptance_rate: float

Accepted rotations per draw, a direct read on how binding the signs are.

Example

Adding a second restricted shock cuts the rate roughly in half here; the rate is the Haar measure of the admissible region, up to the column flips the sampler allows:

>>> import numpy as np
>>> from cultivars.multivariate.reduced_form.vector_autoregression import VAR
>>> rng = np.random.default_rng(0)
>>> B = np.array([[1.0, 0.5], [0.3, 1.0]])
>>> y = np.zeros((402, 2))
>>> for t in range(1, 402):
...     y[t] = 0.4 * y[t - 1] + B @ rng.standard_normal(2)
>>> res = VAR(y, order=1, names=("gdp", "infl")).fit()
>>> one = {"demand": {"gdp": "+", "infl": "+"}}
>>> two = {**one, "supply": {"gdp": "+", "infl": "-"}}
>>> a = SignRestrictedSVAR(res, one, draws=200, seed=0).identify().acceptance_rate
>>> b = SignRestrictedSVAR(res, two, draws=200, seed=0).identify().acceptance_rate
>>> bool(0.0 < b < a <= 1.0)
True