PerturbationDSGEResult._from_fit#

classmethod PerturbationDSGEResult._from_fit(fit, model)[source]#

Assemble the public result from a raw fit and its specification.

Parameters:

  • fit (_PerturbationFit) – The packed fit: estimate, solution, criterion, filter name, effective sample, parameter count and filtered states.

  • model (PerturbationDSGE) – The model that produced it, kept on the result as its engine.

Return type:

PerturbationDSGEResult

Returns:

A populated result.

Example

>>> import numpy as np
>>> class Latent:
...     parameter_names, bounds = ("rho", "sigma"), ((0.0, 0.99), (0.01, 1.0))
...     n_states, n_controls = 1, 1
...     def equations(self, th, y_next, y, x_next, x):
...         return np.array([y[0] - x[0], x_next[0] - th[0] * x[0]])
...     def steady_state(self, th):
...         return np.zeros(1), np.zeros(1)
...     def shock_loading(self, th):
...         return np.array([[th[1]]])
...     def observation(self, th):
...         return np.array([[0.0, 1.0]]), np.zeros(1), np.array([[1e-4]])
...     def log_prior(self, th):
...         return 0.0
>>> rng = np.random.default_rng(0)
>>> model = PerturbationDSGE(Latent(), rng.standard_normal(80), order=1)
>>> fit = model._fit_point(
...     np.array([0.5, 0.5]), filter_name="kalman", n_particles=0, seed=None
... )
>>> res = PerturbationDSGEResult._from_fit(fit, model)
>>> res._engine is model, res.filter, res.order
(True, 'kalman', 1)