cycles=0) # This is what makes the type infinite horizon
BasicType(vFuncBool=False, cubicBool=True)
BasicType.IncomeDstn = [
BasicType.IncomeDstn[0]
] # A "one period" infinite horizon model, so only need one period of income distributions
# Solve the basic type and plot the results, to make sure things are working
start_time = clock()
BasicType.solve()
BasicType.unpack_cFunc()
end_time = clock()
print('Solving the basic consumer took ' + mystr(end_time - start_time) +
' seconds.')
BasicType.unpack_cFunc()
print('Consumption function:')
plotFunc(BasicType.cFunc[0], 0, 5) # plot consumption
print('Marginal consumption function:')
plotFuncDer(BasicType.cFunc[0], 0, 5) # plot MPC
if BasicType.vFuncBool:
print('Value function:')
plotFunc(BasicType.solution[0].vFunc, 0.2, 5)
# Make many copies of the basic type, each with a different risk aversion
BasicType.vFuncBool = False # just in case it was set to True above
my_agent_list = []
CRRA_list = np.linspace(
1, 8, type_count) # All the values that CRRA will take on
for i in range(type_count):
this_agent = deepcopy(BasicType) # Make a new copy of the basic type
this_agent.assignParameters(
CRRA=CRRA_list[i]) # Give it a unique CRRA value
Params.init_consumer_objects['beta'] = 0.96
TestType = RetiringConsumerType(**Params.init_consumer_objects)
TestType(survival_prob = 0.9,
beta = 0.96,
Gamma = 1.00,
rho = 2.0,
sigma_epsilon = [0.0001,None],
state_grid = setupGridsExpMult(0.001, 50, 64, 3),
a_max = 20,
a_size = 48,
calc_vFunc = True,
cubic_splines = False,
constrained = True,
income_distrib = [None,TestType.income_distrib],
p_zero_income = [None,TestType.p_zero_income],
labor_supply = 0.5,
alpha = 1.0,
tolerance = 0.0001,
cycles=0)
TestType.updateAssetsGrid()
transformations = makeCRRAtransformations(TestType.rho,do_Q=True,do_T=True,do_Z=True)
TestType(transformations = transformations)
t_start = time()
TestType.solve()
t_end = time()
print('Took ' + str(t_end-t_start) + ' seconds to solve retirement choice problem.')
plotFuncs(TestType.solution[1].vFunc,20,100)
plotFunc(TestType.solution[0].vFunc,20,100)
BasicType.assignParameters( LivPrb = [0.98],
DiscFac = [0.96],
PermGroFac = [1.01],
cycles = 0) # This is what makes the type infinite horizon
BasicType(vFuncBool = False, cubicBool = True)
BasicType.IncomeDstn = [BasicType.IncomeDstn[0]] # A "one period" infinite horizon model, so only need one period of income distributions
# Solve the basic type and plot the results, to make sure things are working
start_time = clock()
BasicType.solve()
BasicType.unpack_cFunc()
end_time = clock()
print('Solving the basic consumer took ' + mystr(end_time-start_time) + ' seconds.')
BasicType.unpack_cFunc()
print('Consumption function:')
plotFunc(BasicType.cFunc[0],0,5) # plot consumption
print('Marginal consumption function:')
plotFuncDer(BasicType.cFunc[0],0,5) # plot MPC
if BasicType.vFuncBool:
print('Value function:')
plotFunc(BasicType.solution[0].vFunc,0.2,5)
# Make many copies of the basic type, each with a different risk aversion
BasicType.vFuncBool = False # just in case it was set to True above
my_agent_list = []
CRRA_list = np.linspace(1,8,type_count) # All the values that CRRA will take on
for i in range(type_count):
this_agent = deepcopy(BasicType) # Make a new copy of the basic type
this_agent.assignParameters(CRRA = CRRA_list[i]) # Give it a unique CRRA value
my_agent_list.append(this_agent) # Addd it to the list of agent types
MarkovType = ConsumerType(**init_consumer_objects)
transition_matrix = np.array([[1.0 - base_primitives["mho"], base_primitives["mho"]], [0.0, 1.0]])
employed_income_dist = [np.ones(1), np.ones(1), np.ones(1)]
unemployed_income_dist = [np.ones(1), np.ones(1), np.zeros(1)]
p_zero_income = [np.array([0.0, 1.0])]
MarkovType.solution_terminal.cFunc = 2 * [MarkovType.solution_terminal.cFunc]
MarkovType.solution_terminal.vFunc = 2 * [MarkovType.solution_terminal.vFunc]
MarkovType.solution_terminal.vPfunc = 2 * [MarkovType.solution_terminal.vPfunc]
MarkovType.solution_terminal.vPPfunc = 2 * [MarkovType.solution_terminal.vPPfunc]
MarkovType.solution_terminal.m_underbar = 2 * [MarkovType.solution_terminal.m_underbar]
MarkovType.income_distrib = [[employed_income_dist, unemployed_income_dist]]
MarkovType.p_zero_income = p_zero_income
MarkovType.transition_matrix = transition_matrix
MarkovType.time_inv.append("transition_matrix")
MarkovType.solveAPeriod = consumptionSavingSolverMarkov
MarkovType.cycles = 0
t_start = clock()
MarkovType.solve()
t_end = clock()
MarkovType.unpack_cFunc()
print('Solving the same model "the long way" took ' + str(t_end - t_start) + " seconds.")
# plotFuncs([ExampleType.solution[0].cFunc,ExampleType.solution[0].cFunc_U],0,m_upper)
plotFuncs(MarkovType.cFunc[0], 0, m_upper)
diffFunc = lambda m: ExampleType.solution[0].cFunc(m) - MarkovType.cFunc[0][0](m)
print("Difference between the (employed) consumption functions:")
plotFunc(diffFunc, 0, 100)
plotFuncs([ExampleType.cSSfunc, ExampleType.mSSfunc, ExampleType.solution[0].cFunc], 0, 10)
