class testPerfForesightConsumerType(unittest.TestCase):
def setUp(self):
self.agent = PerfForesightConsumerType()
self.agent_infinite = PerfForesightConsumerType(cycles=0)
PF_dictionary = {
"CRRA": 2.5,
"DiscFac": 0.96,
"Rfree": 1.03,
"LivPrb": [0.98],
"PermGroFac": [1.01],
"T_cycle": 1,
"cycles": 0,
"AgentCount": 10000,
}
self.agent_alt = PerfForesightConsumerType(**PF_dictionary)
def test_default_solution(self):
self.agent.solve()
c = self.agent.solution[0].cFunc
self.assertEqual(c.x_list[0], -0.9805825242718447)
self.assertEqual(c.x_list[1], 0.01941747572815533)
self.assertEqual(c.y_list[0], 0)
self.assertEqual(c.y_list[1], 0.511321002804608)
self.assertEqual(c.decay_extrap, False)
def test_another_solution(self):
self.agent_alt.DiscFac = 0.90
self.agent_alt.solve()
self.assertAlmostEqual(self.agent_alt.solution[0].cFunc(10).tolist(),
3.9750093524820787)
def test_check_conditions(self):
self.agent_infinite.check_conditions()
self.assertTrue(self.agent_infinite.conditions["AIC"])
self.assertTrue(self.agent_infinite.conditions["GICRaw"])
self.assertTrue(self.agent_infinite.conditions["RIC"])
self.assertTrue(self.agent_infinite.conditions["FHWC"])
def test_simulation(self):
self.agent_infinite.solve()
# Create parameter values necessary for simulation
SimulationParams = {
"AgentCount": 10000, # Number of agents of this type
"T_sim": 120, # Number of periods to simulate
"aNrmInitMean": -6.0, # Mean of log initial assets
"aNrmInitStd": 1.0, # Standard deviation of log initial assets
"pLvlInitMean": 0.0, # Mean of log initial permanent income
"pLvlInitStd":
0.0, # Standard deviation of log initial permanent income
"PermGroFacAgg": 1.0, # Aggregate permanent income growth factor
"T_age":
None, # Age after which simulated agents are automatically killed
}
self.agent_infinite.assign_parameters(
**SimulationParams
) # This implicitly uses the assign_parameters method of AgentType
# Create PFexample object
self.agent_infinite.track_vars = ["mNrm"]
self.agent_infinite.initialize_sim()
self.agent_infinite.simulate()
self.assertAlmostEqual(
np.mean(self.agent_infinite.history["mNrm"], axis=1)[40],
-23.008063500363942,
)
self.assertAlmostEqual(
np.mean(self.agent_infinite.history["mNrm"], axis=1)[100],
-27.164608851546927,
)
## Try now with the manipulation at time step 80
self.agent_infinite.initialize_sim()
self.agent_infinite.simulate(80)
# This actually does nothing because aNrmNow is
# epiphenomenal. Probably should change mNrmNow instead
self.agent_infinite.state_now['aNrm'] += (-5.0)
self.agent_infinite.simulate(40)
self.assertAlmostEqual(
np.mean(self.agent_infinite.history["mNrm"], axis=1)[40],
-23.008063500363942,
)
self.assertAlmostEqual(
np.mean(self.agent_infinite.history["mNrm"], axis=1)[100],
-29.140261331951606,
)
def test_stable_points(self):
# Solve the constrained agent. Stable points exists only with a
# borrowing constraint.
constrained_agent = PerfForesightConsumerType(cycles=0,
BoroCnstArt=0.0)
constrained_agent.solve()
# Check against pre-computed values.
self.assertEqual(constrained_agent.solution[0].mNrmStE, 1.0)
# Check that they are both the same, since the problem is deterministic
self.assertEqual(constrained_agent.solution[0].mNrmStE,
constrained_agent.solution[0].mNrmTrg)
class testPerfForesightConsumerType(unittest.TestCase):
def setUp(self):
self.agent = PerfForesightConsumerType()
self.agent_infinite = PerfForesightConsumerType(cycles=0)
PF_dictionary = {
'CRRA': 2.5,
'DiscFac': 0.96,
'Rfree': 1.03,
'LivPrb': [0.98],
'PermGroFac': [1.01],
'T_cycle': 1,
'cycles': 0,
'AgentCount': 10000
}
self.agent_alt = PerfForesightConsumerType(**PF_dictionary)
def test_default_solution(self):
self.agent.solve()
c = self.agent.solution[0].cFunc
self.assertEqual(c.x_list[0], -0.9805825242718447)
self.assertEqual(c.x_list[1], 0.01941747572815533)
self.assertEqual(c.y_list[0], 0)
self.assertEqual(c.y_list[1], 0.511321002804608)
self.assertEqual(c.decay_extrap, False)
def test_another_solution(self):
self.agent_alt.DiscFac = 0.90
self.agent_alt.solve()
self.assertAlmostEqual(self.agent_alt.solution[0].cFunc(10).tolist(),
3.9750093524820787)
def test_checkConditions(self):
self.agent_infinite.checkConditions()
self.assertTrue(self.agent_infinite.conditions['AIC'])
self.assertTrue(self.agent_infinite.conditions['GICPF'])
self.assertTrue(self.agent_infinite.conditions['RIC'])
self.assertTrue(self.agent_infinite.conditions['FHWC'])
def test_simulation(self):
self.agent_infinite.solve()
# Create parameter values necessary for simulation
SimulationParams = {
"AgentCount": 10000, # Number of agents of this type
"T_sim": 120, # Number of periods to simulate
"aNrmInitMean": -6.0, # Mean of log initial assets
"aNrmInitStd": 1.0, # Standard deviation of log initial assets
"pLvlInitMean": 0.0, # Mean of log initial permanent income
"pLvlInitStd":
0.0, # Standard deviation of log initial permanent income
"PermGroFacAgg": 1.0, # Aggregate permanent income growth factor
"T_age":
None, # Age after which simulated agents are automatically killed
}
self.agent_infinite(
**SimulationParams
) # This implicitly uses the assignParameters method of AgentType
# Create PFexample object
self.agent_infinite.track_vars = ['mNrmNow']
self.agent_infinite.initializeSim()
self.agent_infinite.simulate()
self.assertAlmostEqual(
np.mean(self.agent_infinite.history['mNrmNow'], axis=1)[40],
-23.008063500363942)
self.assertAlmostEqual(
np.mean(self.agent_infinite.history['mNrmNow'], axis=1)[100],
-27.164608851546927)
## Try now with the manipulation at time step 80
self.agent_infinite.initializeSim()
self.agent_infinite.simulate(80)
self.agent_infinite.aNrmNow += -5. # Adjust all simulated consumers' assets downward by 5
self.agent_infinite.simulate(40)
self.assertAlmostEqual(
np.mean(self.agent_infinite.history['mNrmNow'], axis=1)[40],
-23.008063500363942)
self.assertAlmostEqual(
np.mean(self.agent_infinite.history['mNrmNow'], axis=1)[100],
-29.140261331951606)
# %% [markdown] pycharm={"name": "#%% md\n"}
# To generate simulated data, we need to specify which variables we want to track the "history" of for this instance. To do so, we set the $\texttt{track_vars}$ attribute of our $\texttt{PerfForesightConsumerType}$ instance to be a list of strings with the simulation variables we want to track.
#
# In this model, valid elments of $\texttt{track_vars}$ include $\texttt{mNrmNow}$, $\texttt{cNrmNow}$, $\texttt{aNrmNow}$, and $\texttt{pLvlNow}$. Because this model has no idiosyncratic shocks, our simulated data will be quite boring.
#
# ### Generating simulated data
#
# Before simulating, the $\texttt{initializeSim}$ method must be invoked. This resets our instance back to its initial state, drawing a set of initial $\texttt{aNrmNow}$ and $\texttt{pLvlNow}$ values from the specified distributions and storing them in the attributes $\texttt{aNrmNow_init}$ and $\texttt{pLvlNow_init}$. It also resets this instance's internal random number generator, so that the same initial states will be set every time $\texttt{initializeSim}$ is called. In models with non-trivial shocks, this also ensures that the same sequence of shocks will be generated on every simulation run.
#
# Finally, the $\texttt{simulate}$ method can be called.
# %% pycharm={"name": "#%%\n"}
PFexample.track_vars = ["mNrmNow"]
PFexample.initializeSim()
PFexample.simulate()
# %% [markdown] pycharm={"name": "#%% md\n"}
# Each simulation variable $\texttt{X}$ named in $\texttt{track_vars}$ will have the *history* of that variable for each agent stored in the attribute $\texttt{X_hist}$ as an array of shape $(\texttt{T_sim},\texttt{AgentCount})$. To see that the simulation worked as intended, we can plot the mean of $m_t$ in each simulated period:
# %% pycharm={"name": "#%%\n"}
plt.plot(np.mean(PFexample.history["mNrmNow"], axis=1))
plt.xlabel("Time")
plt.ylabel("Mean normalized market resources")
plt.show()
# %% [markdown] pycharm={"name": "#%% md\n"}
# A perfect foresight consumer can borrow against the PDV of his future income-- his human wealth-- and thus as time goes on, our simulated agents approach the (very negative) steady state level of $m_t$ while being steadily replaced with consumers with roughly $m_t=1$.
#
# The slight wiggles in the plotted curve are due to consumers randomly dying and being replaced; their replacement will have an initial state drawn from the distributions specified by the user. To see the current distribution of ages, we can look at the attribute $\texttt{t_age}$.
mystr(end_time - start_time) + " seconds.")
PFexample.unpackcFunc()
PFexample.timeFwd()
# %%
# Plot the perfect foresight consumption function
print("Perfect foresight consumption function:")
mMin = PFexample.solution[0].mNrmMin
plotFuncs(PFexample.cFunc[0], mMin, mMin + 10)
# %%
if do_simulation:
PFexample.T_sim = 120 # Set number of simulation periods
PFexample.track_vars = ["mNrmNow"]
PFexample.initializeSim()
PFexample.simulate()
# %%
# Make and solve an example consumer with idiosyncratic income shocks
IndShockExample = IndShockConsumerType()
IndShockExample.cycles = 0 # Make this type have an infinite horizon
# %%
start_time = time()
IndShockExample.solve()
end_time = time()
print("Solving a consumer with idiosyncratic shocks took " +
mystr(end_time - start_time) + " seconds.")
IndShockExample.unpackcFunc()
IndShockExample.timeFwd()