class testAggShockConsumerType(unittest.TestCase):
def setUp(self):
self.agent = AggShockConsumerType()
self.agent.cycles = 0
self.economy = EconomyExample = CobbDouglasEconomy(
agents=[self.agent])
def test_economy(self):
# Make a Cobb-Douglas economy for the agents
self.economy.makeAggShkHist() # Simulate a history of aggregate shocks
# Have the consumers inherit relevant objects from the economy
self.agent.getEconomyData(self.economy)
self.agent.solve()
if solve_agg_shocks_micro or solve_agg_shocks_market:
# Make an aggregate shocks consumer type
AggShockExample = AggShockConsumerType()
AggShockExample.cycles = 0
# Make a Cobb-Douglas economy for the agents
EconomyExample = CobbDouglasEconomy(agents=[AggShockExample])
EconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks
# Have the consumers inherit relevant objects from the economy
AggShockExample.getEconomyData(EconomyExample)
if solve_agg_shocks_micro:
# Solve the microeconomic model for the aggregate shocks example type (and display results)
t_start = process_time()
AggShockExample.solve()
t_end = process_time()
print("Solving an aggregate shocks consumer took " +
mystr(t_end - t_start) + " seconds.")
print(
"Consumption function at each aggregate market resources-to-labor ratio gridpoint:"
)
m_grid = np.linspace(0, 10, 200)
AggShockExample.unpack('cFunc')
for M in AggShockExample.Mgrid.tolist():
mMin = AggShockExample.solution[0].mNrmMin(M)
c_at_this_M = AggShockExample.cFunc[0](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()
