dr = time_iteration(hmodel.agent, maxit=100, verbose=True) sol = improved_time_iteration(hmodel.agent, dr0=dr, verbose=True) dr = sol.dr # %% from dolo.algos.ergodic import ergodic_distribution μ = ergodic_distribution(hmodel.agent, dr)[1] from matplotlib import pyplot as plt plt.plot(μ.data.ravel()) # %% from dolark.equilibrium import find_steady_state eq = find_steady_state(hmodel, verbose=True, dr0=dr) # %% from matplotlib import pyplot as plt plt.plot(eq.μ) # %% # %%
def test_steady_state_non_ex_ante_ha():
hmodel = HModel("ayiagari.yaml")
eq = find_steady_state(hmodel)
hmodel = HModel("bfs_2017.yaml")
eq = find_steady_state(hmodel)
groot("examples")
from dolark import HModel
# %%
hmodel1 = HModel("ayiagari.yaml")
print(hmodel1.name)
hmodel2 = HModel("ayiagari_betadist.yaml")
print(hmodel2.name)
hmodel3 = HModel("bfs_2017.yaml")
print(hmodel3.name)
# %%
eq1 = find_steady_state(hmodel1)
eq2 = find_steady_state(hmodel2)
eq3 = find_steady_state(hmodel3)
# %%
hmodel = HModel("prototype.yaml")
eq = find_steady_state(hmodel)
# %%
# Decision rules
# Aiyagari
from matplotlib import pyplot as plt
s = eq1.dr.endo_grid.nodes
plt.plot(s, eq1.dr(0, s), color="black")
plt.plot(s, s, linestyle="--", color="black")
# %%
# Aiyagari with beta
def test_steady_state_ex_ante():
hmodel = HModel("ayiagari_betadist.yaml")
eq = find_steady_state(hmodel)
def test_with_agg_states():
hmodel = HModel("prototype.yaml")
eq = find_steady_state(hmodel)
import dolark
from dolark import HModel # The model is written in yaml file, HModel is used to read the yaml file
from dolark.equilibrium import find_steady_state
from dolark.perturbation import perturb
from dolo import time_iteration, improved_time_iteration
from matplotlib import pyplot as plt
import numpy as np
#HModel reads the yaml file
aggmodel = HModel('Aiyagari.yaml')
aggmodel
# check features of the model
aggmodel.features
eq = find_steady_state(aggmodel)
eq
#plot the wealth distribution
s = eq.dr.endo_grid.nodes(
) # grid for states (i.e the state variable--wealth in this case)
plt.plot(s, eq.μ.sum(axis=0), color='black')
plt.grid()
plt.title("Wealth Distribution")
# You can also check the steady state values of all variables
eq.as_df()
# define the dataframe
df = eq.as_df()
dr = sol.dr #%% from dolo.algos.ergodic import ergodic_distribution μ = ergodic_distribution(hmodel.agent, dr)[1] #%% from matplotlib import pyplot as plt plt.plot(μ.data.ravel()) #%% from dolark.equilibrium import find_steady_state eqs = find_steady_state(hmodel, dr0=dr, verbose='full', return_fun=False) #%% m0 = hmodel.calibration['exogenous'] y0 = hmodel.calibration['aggregate'] p0 = hmodel.calibration['parameters'] (m0, y0, hmodel.projection(m0, y0, p0)) # %% from matplotlib import pyplot as plt from dolo import tabulate tab = tabulate(hmodel.agent, dr, "m")
print(hmodel2.features)
print(hmodel2.distribution)
hmodel3 = HModel('bfs_2017.yaml')
print(hmodel3.name)
print(hmodel3.features)
# print(hmodel3.distribution)
# # Identical Agents: autocorrelated procesess
# +
# the agent's problem has autocorrelated exogenous process
# it is discretized as an markov chain
# -
eq = find_steady_state(hmodel1)
eq
# a bit out of topic here:
from dolark.perturbation import perturb
peq = perturb(hmodel1, eq)
peq
# +
# cf reiter_example to see what to do with eq and peq
# -
# # Many Agents: autocorrelated procesess
# distribution of agent's parameters
hmodel2.distribution
# %%
from dolo import groot
groot("examples")
from dolark import HModel
from dolark.equilibrium import find_steady_state
hmodel = HModel("ayiagari_betadist.yaml")
eq = find_steady_state(hmodel)
# hmodel3 = HModel('bfs_2017.yaml')
# print(hmodel3.name)
#%%
#%%
#%%
# dr0 = hmodel2.get_starting_rule()
# m0, y0 = hmodel2.calibration['exogenous','aggregate']
# eq = equilibrium(hmodel2, m0, y0, dr0=dr0)
# eq = find_steady_state(hmodel1, dr0=dr0)
# #%%
eq0 = find_steady_state(hmodel1)
for j in range(3):
plt.plot(w * eq0.μ[j, :], label=f"{i}")
#%%
eqss = find_steady_state(hmodel2)
from matplotlib import pyplot as plt
for i, (w, eq) in enumerate(eqss):
s = eq.dr.endo_grid.nodes()
for j in range(3):
plt.plot(s, w * eq.μ[j, :], label=f"{i}")
plt.legend()