from dolark import HModel
from dolo.algos import time_iteration, improved_time_iteration
hmodel = HModel("bfs_2017_K.yaml")
hmodel.features
#%%
#%%
m0 = hmodel.calibration["exogenous"]
s0 = hmodel.calibration["states"]
y0 = hmodel.calibration["aggregate"]
p0 = hmodel.calibration["parameters"]
(m0, y0, hmodel.projection(m0, s0, y0, p0))
# %%
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
from dolo import time_iteration, improved_time_iteration
dr = time_iteration(hmodel.agent)
# # %time dr = improved_time_iteration(hmodel.model, dr0=dr, verbose=True, details=False)
from dolo import tabulate
tab = tabulate(hmodel.agent, dr, 'm')
plt.plot(tab['m'], tab['c'])
# ergodic distribution (premature)
Π, μ = ergodic_distribution(hmodel.model, dr)
df_μ = μ.to_dataframe('μ').reset_index()
ch = alt.Chart(tab)
g1 = ch.mark_line(color='black',strokeDash=[1,1]).encode(x='m', y='m') + \
ch.mark_line().encode(x='m', y='c')
g2 = alt.Chart(df_μ).mark_line().encode(x='m:Q', y='mu:Q')
g2
# There seem to be something wrong with the calibration at the aggregate level
# here are the value of i.r. and w calibrated at the agent's level
hmodel.agent.calibration['r', 'w']
# here are the values projected from market equilibrium, given default level of capital
m0, y0, p = hmodel.calibration['exogenous', 'aggregate', 'parameters']
hmodel.projection(m0, y0, p) # values for r, w, ω (not the same at all)