fig = plt.figure()
bottom = 0.1
top = 2*KSEconomy.kSS
x = np.linspace(bottom,top,1000,endpoint=True)
print(KSEconomy.AFunc)
y0 = KSEconomy.AFunc[0](x)
y1 = KSEconomy.AFunc[1](x)
plt.plot(x,y0)
plt.plot(x,y1)
plt.xlim([bottom, top])
make_figs('aggregate_savings', True, False)
# remark.show('aggregate_savings')
print('Consumption function at each aggregate market resources gridpoint (in general equilibrium):')
KSAgent.unpackcFunc()
m_grid = np.linspace(0,10,200)
KSAgent.unpackcFunc()
for M in KSAgent.Mgrid:
c_at_this_M = KSAgent.solution[0].cFunc[0](m_grid,M*np.ones_like(m_grid)) #Have two consumption functions, check this
plt.plot(m_grid,c_at_this_M)
make_figs('consumption_function', True, False)
# remark.show('consumption_function')
print('Savings at each individual market resources gridpoint (in general equilibrium):')
fig = plt.figure()
KSAgent.unpackcFunc()
m_grid = np.linspace(0,10,200)
KSAgent.unpackcFunc()
for M in KSAgent.Mgrid:
# Solve the microeconomic model for the Markov aggregate shocks example type (and display results)
t_start = process_time()
AggShockMrkvExample.solve()
t_end = process_time()
print(
"Solving an aggregate shocks Markov consumer took "
+ mystr(t_end - t_start)
+ " seconds."
)
print(
"Consumption function at each aggregate market \
resources-to-labor ratio gridpoint (for each macro state):"
)
m_grid = np.linspace(0, 10, 200)
AggShockMrkvExample.unpackcFunc()
for i in range(2):
for M in AggShockMrkvExample.Mgrid.tolist():
mMin = AggShockMrkvExample.solution[0].mNrmMin[i](M)
c_at_this_M = AggShockMrkvExample.cFunc[0][i](
m_grid + mMin, M * np.ones_like(m_grid)
)
plt.plot(m_grid + mMin, c_at_this_M)
plt.ylim(0.0, None)
plt.show()
# %%
if solve_markov_market:
# Solve the "macroeconomic" model by searching for a "fixed point dynamic rule"
t_start = process_time()
print("Now solving a two-state Markov economy. This should take a few minutes...")