def test_ergo_mc():
from dolo import yaml_import, improved_time_iteration, ergodic_distribution
model = yaml_import("examples/models/rbc_mc.yaml")
sol = improved_time_iteration(model)
Π, μ = ergodic_distribution(model, sol.dr)
assert (μ.ndim == 2)
def equilibrium(
hmodel,
m0: "vector",
S0=None,
X0: "vector" = None,
p=None,
dr0=None,
grids=None,
verbose=False,
return_equilibrium=True,
):
if p is None:
p = hmodel.calibration["parameters"]
if S0 is None:
q0 = hmodel.projection(m0, X0, p)
else:
q0 = hmodel.projection(m0, S0, X0, p)
dp = inject_process(q0, hmodel.model.exogenous)
sol = time_iteration(hmodel.model,
dr0=dr0,
dprocess=dp,
maxit=10,
verbose=verbose)
sol = improved_time_iteration(hmodel.model,
dr0=sol,
dprocess=dp,
verbose=verbose)
dr = sol.dr
if grids is None:
exg, edg = grids = dr.exo_grid, dr.endo_grid
else:
exg, edg = grids
Π0, μ0 = ergodic_distribution(hmodel.model, dr, exg, edg, dp)
s = edg.nodes
if exg.n_nodes == 0:
nn = 1
μμ0 = μ0.data[None, :]
else:
nn = exg.n_nodes
μμ0 = μ0.data
xx0 = np.concatenate(
[e[None, :, :] for e in [dr(i, s) for i in range(nn)]], axis=0)
res = hmodel.𝒜(grids, m0, μμ0, xx0, X0, m0, X0, p, S0=S0, S1=S0)
if return_equilibrium:
return (res, sol, μ0, Π0)
else:
return res
def get_starting_rule(self, method="improved_time_iteration", **kwargs):
# provides initial guess for d.r. by solving agent's problem
dp = self.model.exogenous.discretize()
# dr0 = time_iteration(self.model, dprocess=mc)
if method == "improved_time_iteration":
sol = improved_time_iteration(self.model, dprocess=dp, **kwargs)
elif method == "time_iteration":
sol = time_iteration(self.model, dprocess=dp, **kwargs)
dr0 = sol.dr
return dr0
def get_starting_rule(self, method='improved_time_iteration', **kwargs):
# provides initial guess for d.r. by solving agent's problem
mc = self.model.exogenous.discretize(
to='mc', options=[{}, self.discretization_options])
# dr0 = time_iteration(self.model, dprocess=mc)
if method == 'improved_time_iteration':
sol = improved_time_iteration(self.model, dprocess=mc, **kwargs)
elif method == 'time_iteration':
sol = time_iteration(self.model, dprocess=mc, **kwargs)
dr0 = sol.dr
return dr0