class testAggShockMarkovConsumerType(unittest.TestCase):
def setUp(self):
# Make one agent type and an economy for it to live in
self.agent = AggShockMarkovConsumerType()
self.agent.cycles = 0
self.agent.AgentCount = 1000 # Low number of simulated agents
self.agent.IncomeDstn[0] = 2 * [self.agent.IncomeDstn[0]] ## see #557
self.economy = CobbDouglasMarkovEconomy(agents=[self.agent])
def test_agent(self):
# Have one consumer type inherit relevant objects from the economy,
# then solve their microeconomic problem
self.agent.getEconomyData(self.economy)
self.agent.solve()
self.assertAlmostEqual(
self.agent.solution[0].cFunc[0](10.0, self.economy.MSS),
2.5635896520991377)
def test_economy(self):
# Adjust the economy so that it (fake) solves quickly
self.economy.act_T = 500 # Short simulation history
self.economy.max_loops = 3 # Just quiet solving early
self.economy.verbose = False # Turn off printed messages
self.agent.getEconomyData(self.economy)
self.economy.makeAggShkHist(
) # Make a simulated history of aggregate shocks
self.economy.solve(
) # Solve for the general equilibrium of the economy
self.economy.AFunc = self.economy.dynamics.AFunc
self.assertAlmostEqual(self.economy.AFunc[0].slope, 1.0904698841958917)
self.assertAlmostEqual(self.economy.history["AaggNow"][5],
9.467758924955897)
AggShockMrkvExample.IncomeDstn[0] = 2 * [AggShockMrkvExample.IncomeDstn[0]]
AggShockMrkvExample.cycles = 0
# Make a Cobb-Douglas economy for the agents
MrkvEconomyExample = CobbDouglasMarkovEconomy(agents=[AggShockMrkvExample])
MrkvEconomyExample.DampingFac = 0.2 # Turn down damping
MrkvEconomyExample.makeAggShkHist(
) # Simulate a history of aggregate shocks
AggShockMrkvExample.getEconomyData(
MrkvEconomyExample
) # Have the consumers inherit relevant objects from the economy
if solve_markov_micro:
# 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.unpack('cFunc')
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)