SpectralCausalityResult.pair#

SpectralCausalityResult.pair(cause, effect)[source]#

One directed pair’s measure across the grid.

Parameters:

  • cause (str) – The variable whose history is being credited.

  • effect (str) – The variable whose spectrum is being decomposed.

Return type:

NDArray[double]

Returns:

The (n,) non-negative measure.

Raises:

SpecificationError – If either label is unknown.

Example

>>> import numpy as np
>>> from cultivars.multivariate.reduced_form.vector_autoregression import VAR
>>> rng = np.random.default_rng(0)
>>> y = np.zeros((500, 2))
>>> for t in range(1, 500):
...     y[t, 0] = 0.5 * y[t - 1, 0] + 0.4 * y[t - 1, 1] + rng.standard_normal()
...     y[t, 1] = 0.5 * y[t - 1, 1] + rng.standard_normal()
>>> res = VAR(y, order=1, names=("gdp", "rate")).fit()
>>> causal = SpectralCausality(res, n_frequencies=64).compute()
>>> curve = causal.pair("rate", "gdp")
>>> curve.shape, bool(np.all(curve >= 0.0)), bool(curve[0] > curve[-1])
((64,), True, True)
>>> bool(np.array_equal(curve, causal.measure[:, 0, 1]))
True