#%% 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") plt.plot(tab["m"], tab["m"]) plt.plot(tab["m"], tab["c"]) plt.grid(True) # %% from dolark.perturbation import perturb sol = perturb(hmodel, eqs) # %%
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 dolark.shocks import discretize_idiosyncratic_shocks
dist = discretize_idiosyncratic_shocks(hmodel2.distribution,
options=[{
'N': 6
color=alt.condition(single, 'i_m:N',
alt.value('lightgray'))).add_selection(single)
# %%
# Resulting object can be saved to a file. (try to open this file in jupyterlab)
open('distrib.json', 'tw').write(spec.to_json())
# %%
# %%
import xarray
# %%
# now we compute the perturbation
peq = perturb(aggmodel, eq)
# %%
# and we simulate given initial value of aggregate shock
sim = peq.response([0.1])
# %%
plt.subplot(121)
for t, (m, μ, x, y) in enumerate(sim):
plt.plot(μ.sum(axis=0), color='red', alpha=0.01)
plt.xlabel('a')
plt.ylabel('density')
plt.grid()
plt.subplot(122)
plt.plot([e[3][0] for e in sim])
plt.xlabel("t")
color=alt.condition(single, "i_m:N",
alt.value("lightgray"))).add_selection(single)
# %%
# Resulting object can be saved to a file. (try to open this file in jupyterlab)
open("distrib.json", "tw").write(spec.to_json())
# %%
# %%
import xarray
# %%
# now we compute the perturbation
peq = perturb(aggmodel, eq)
# %%
# and we simulate given initial value of aggregate shock
sim = peq.response([0.1])
# %%
plt.subplot(121)
for t, (m, μ, x, y) in enumerate(sim):
plt.plot(μ.sum(axis=0), color="red", alpha=0.01)
plt.xlabel("a")
plt.ylabel("density")
plt.grid()
plt.subplot(122)
plt.plot([e[3][0] for e in sim])
plt.xlabel("t")