def sim_birth(self, which_agents):
"""
Makes new Markov consumer by drawing initial normalized assets, permanent income levels, and
discrete states. Calls IndShockConsumerType.sim_birth, then draws from initial Markov distribution.
Parameters
----------
which_agents : np.array(Bool)
Boolean array of size self.AgentCount indicating which agents should be "born".
Returns
-------
None
"""
IndShockConsumerType.sim_birth(
self, which_agents
) # Get initial assets and permanent income
if (
not self.global_markov
): # Markov state is not changed if it is set at the global level
N = np.sum(which_agents)
base_draws = Uniform(seed=self.RNG.randint(0, 2 ** 31 - 1)).draw(N)
Cutoffs = np.cumsum(np.array(self.MrkvPrbsInit))
self.shocks["Mrkv"][which_agents] = np.searchsorted(
Cutoffs, base_draws
).astype(int)
def sim_birth(self, which_agents):
"""
Create new agents to replace ones who have recently died; takes draws of
initial aNrm and pLvl, as in ConsIndShockModel, then sets Share and Adjust
to zero as initial values.
Parameters
----------
which_agents : np.array
Boolean array of size AgentCount indicating which agents should be "born".
Returns
-------
None
"""
IndShockConsumerType.sim_birth(self, which_agents)
self.controls["Share"][which_agents] = 0
# here a shock is being used as a 'post state'
self.shocks["Adjust"][which_agents] = False
class testIndShockConsumerType(unittest.TestCase):
def setUp(self):
self.agent = IndShockConsumerType(AgentCount=2, T_sim=10)
self.agent.solve()
def test_get_shocks(self):
self.agent.initialize_sim()
self.agent.sim_birth(np.array([True, False]))
self.agent.sim_one_period()
self.agent.sim_birth(np.array([False, True]))
self.agent.get_shocks()
self.assertEqual(self.agent.shocks['PermShk'][0], 1.0427376294215103)
self.assertAlmostEqual(self.agent.shocks['PermShk'][1],
0.9278094171517413)
self.assertAlmostEqual(self.agent.shocks['TranShk'][0],
0.881761797501595)
def test_ConsIndShockSolverBasic(self):
LifecycleExample = IndShockConsumerType(**init_lifecycle)
LifecycleExample.cycles = 1
LifecycleExample.solve()
# test the solution_terminal
self.assertAlmostEqual(LifecycleExample.solution[-1].cFunc(2).tolist(),
2)
self.assertAlmostEqual(LifecycleExample.solution[9].cFunc(1),
0.79429538)
self.assertAlmostEqual(LifecycleExample.solution[8].cFunc(1),
0.79391692)
self.assertAlmostEqual(LifecycleExample.solution[7].cFunc(1),
0.79253095)
self.assertAlmostEqual(LifecycleExample.solution[0].cFunc(1).tolist(),
0.7506184692092213)
self.assertAlmostEqual(LifecycleExample.solution[1].cFunc(1).tolist(),
0.7586358637239385)
self.assertAlmostEqual(LifecycleExample.solution[2].cFunc(1).tolist(),
0.7681247572911291)
solver = ConsIndShockSolverBasic(
LifecycleExample.solution[1],
LifecycleExample.IncShkDstn[0],
LifecycleExample.LivPrb[0],
LifecycleExample.DiscFac,
LifecycleExample.CRRA,
LifecycleExample.Rfree,
LifecycleExample.PermGroFac[0],
LifecycleExample.BoroCnstArt,
LifecycleExample.aXtraGrid,
LifecycleExample.vFuncBool,
LifecycleExample.CubicBool,
)
solver.prepare_to_solve()
self.assertAlmostEqual(solver.DiscFacEff, 0.9586233599999999)
self.assertAlmostEqual(solver.PermShkMinNext, 0.6554858756904397)
self.assertAlmostEqual(solver.cFuncNowCnst(4).tolist(), 4.0)
self.assertAlmostEqual(solver.prepare_to_calc_EndOfPrdvP()[0],
-0.19792871012285213)
self.assertAlmostEqual(solver.prepare_to_calc_EndOfPrdvP()[-1],
19.801071289877118)
EndOfPrdvP = solver.calc_EndOfPrdvP()
self.assertAlmostEqual(EndOfPrdvP[0], 6657.839372100613)
self.assertAlmostEqual(EndOfPrdvP[-1], 0.2606075215645896)
solution = solver.make_basic_solution(EndOfPrdvP, solver.aNrmNow,
solver.make_linear_cFunc)
solver.add_MPC_and_human_wealth(solution)
self.assertAlmostEqual(solution.cFunc(4).tolist(), 1.0028005137373956)
def test_simulated_values(self):
self.agent.initialize_sim()
self.agent.simulate()
self.assertAlmostEqual(self.agent.MPCnow[1], 0.5711503906043797)
self.assertAlmostEqual(self.agent.state_now['aLvl'][1],
0.18438326264597635)