MinnesotaPrior.coefficient_mean#

MinnesotaPrior.coefficient_mean(context)[source]#

Each variable’s own first lag at persistence, everything else zero.

The random-walk centre: row lag_offset + j of column j holds that variable’s persistence, and every other entry – the deterministic block, the cross lags, the longer own lags, the exogenous block – is zero.

Parameters:

context (_PriorContext) – The sample description; k_endog, width, and lag_offset are read.

Return type:

NDArray[double]

Returns:

A (width, k) array in design-column order.

Raises:

Example

>>> import numpy as np
>>> from cultivars.bayes.priors import PriorContext
>>> ctx = PriorContext(
...     k_endog=2, order=1, scales=np.ones(2), presample_mean=np.zeros(2)
... )
>>> MinnesotaPrior(persistence=(1.0, 0.0)).coefficient_mean(ctx)
array([[0., 0.],
       [1., 0.],
       [0., 0.]])