class testKrusellSmith(unittest.TestCase):
def setUp(self):
# Make one agent type and an economy for it to live in
self.agent = KrusellSmithType()
self.agent.cycles = 0
self.agent.AgentCount = 1000 # Low number of simulated agents
self.economy = KrusellSmithEconomy(agents=[self.agent])
self.economy.max_loops = 2 # Quit early
self.economy.act_T = 1100 # Shorter simulated period
self.economy.discard_periods = 100
self.economy.verbose = False # Turn off printed messages
def test_agent(self):
self.agent.getEconomyData(self.economy)
self.agent.solve()
self.assertAlmostEqual(
self.agent.solution[0].cFunc[0](10., self.economy.MSS), 1.23867751)
def test_economy(self):
self.agent.getEconomyData(self.economy)
self.economy.makeMrkvHist(
) # 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, 1.0014463644834282)
self.assertAlmostEqual(self.economy.history['Aprev'][4],
11.009107526443584)
self.assertAlmostEqual(self.economy.history['MrkvNow'][40], 1)
self.assertAlmostEqual(self.economy.history['Urate'][12],
0.040000000000000036)
#
# More recently, we have added a model to HARK that exactly replicates the Krusell-Smith model (without dynastic discount factor heterogeneity). In the cells below, we present a basic example of using this model. The default parameters are taken directly from Krusell & Smith (1998) and their results are replicated almost exactly.
# %%
# Import Krusell-Smith model
from HARK.ConsumptionSaving.ConsAggShockModel import KrusellSmithType, KrusellSmithEconomy
from time import time
from scipy.stats import linregress
# %%
# Make default KS agent type and economy
KSeconomy = KrusellSmithEconomy()
KSeconomy.verbose = False
KStype = KrusellSmithType()
KStype.cycles = 0
KStype.getEconomyData(KSeconomy)
KSeconomy.agents = [KStype]
KSeconomy.makeMrkvHist()
# %%
# Solve the Krusell-Smith economy
t0 = time()
print("Now solving for the equilibrium of the Krusell-Smith (1998) model. This might take a few minutes...")
KSeconomy.solve()
t1 = time()
print('Solving the Krusell-Smith model took ' + str(t1-t0) + ' seconds.')
# %%
# Plot the consumption function for each discrete state
state_names = ['bad economy, unemployed', 'bad economy, employed',
'good economy, unemployed', 'good economy, employed']