def initializeSim(self):
IndShockConsumerType.initializeSim(self)
if self.global_markov: #Need to initialize markov state to be the same for all agents
base_draw = drawUniform(1,seed=self.RNG.randint(0,2**31-1))
Cutoffs = np.cumsum(np.array(self.MrkvPrbsInit))
self.MrkvNow = np.ones(self.AgentCount)*np.searchsorted(Cutoffs,base_draw).astype(int)
self.MrkvNow = self.MrkvNow.astype(int)
def __init__(self,
cycles=1,
**kwds):
'''
Instantiate a new ConsumerType with given data, and construct objects
to be used during solution (income distribution, assets grid, etc).
See ConsumerParameters.init_pref_shock for a dictionary of
the keywords that should be passed to the constructor.
Parameters
----------
cycles : int
Number of times the sequence of periods should be solved.
Returns
-------
None
'''
params = init_preference_shocks.copy()
params.update(kwds)
IndShockConsumerType.__init__(self,
cycles=cycles,
**params)
self.solveOnePeriod = makeOnePeriodOOSolver(ConsPrefShockSolver)
def __init__(self, cycles=1, time_flow=True, **kwds):
'''
Instantiate a new consumer type with given data.
See ConsumerParameters.init_labor_intensive for a dictionary of
the keywords that should be passed to the constructor.
Parameters
----------
cycles : int
Number of times the sequence of periods should be solved.
time_flow : boolean
Whether time is currently "flowing" forward for this instance.
Returns
-------
None
'''
params = Params.init_labor_intensive.copy()
params.update(kwds)
kwds = params
IndShockConsumerType.__init__(self,
cycles=cycles,
time_flow=time_flow,
**kwds)
self.pseudo_terminal = False
self.solveOnePeriod = solveConsLaborIntMarg
self.update()
def test_ConsIndShockSolverBasic(self):
LifecycleExample = IndShockConsumerType(**init_lifecycle)
LifecycleExample.cycles = 1
LifecycleExample.solve()
solver = ConsIndShockSolverBasic(
LifecycleExample.solution[1], LifecycleExample.IncomeDstn[0],
LifecycleExample.LivPrb[0], LifecycleExample.DiscFac,
LifecycleExample.CRRA, LifecycleExample.Rfree,
LifecycleExample.PermGroFac[0], LifecycleExample.BoroCnstArt,
LifecycleExample.aXtraGrid, LifecycleExample.vFuncBool,
LifecycleExample.CubicBool)
solver.prepareToSolve()
self.assertAlmostEqual(solver.DiscFacEff, 0.9503999999999999)
self.assertAlmostEqual(solver.PermShkMinNext, 0.850430160026919)
self.assertAlmostEqual(solver.cFuncNowCnst(4).tolist(), 4.0)
self.assertAlmostEqual(solver.prepareToCalcEndOfPrdvP()[0],
-0.2491750859108316)
self.assertAlmostEqual(solver.prepareToCalcEndOfPrdvP()[-1],
19.74982491408914)
EndOfPrdvP = solver.calcEndOfPrdvP()
self.assertAlmostEqual(EndOfPrdvP[0], 6622.251864311334)
self.assertAlmostEqual(EndOfPrdvP[-1], 0.026301061207747087)
solution = solver.makeBasicSolution(EndOfPrdvP, solver.aNrmNow,
solver.makeLinearcFunc)
solver.addMPCandHumanWealth(solution)
self.assertAlmostEqual(solution.cFunc(4).tolist(), 1.7391265696400773)
def setUp(self):
self.agent = IndShockConsumerType(
AgentCount = 2,
T_sim = 10
)
self.agent.solve()
def test_cyclical(self):
CyclicalExample = IndShockConsumerType(**CyclicalDict)
CyclicalExample.cycles = 0 # Make this consumer type have an infinite horizon
CyclicalExample.solve()
self.assertAlmostEqual(CyclicalExample.solution[3].cFunc(3).tolist(),
1.5958390056965004)
def __init__(self, cycles=1, time_flow=True, **kwds):
'''
Instantiate a new ConsumerType with given data, and construct objects
to be used during solution (income distribution, assets grid, etc).
See ConsumerParameters.init_pref_shock for a dictionary of
the keywords that should be passed to the constructor.
Parameters
----------
cycles : int
Number of times the sequence of periods should be solved.
time_flow : boolean
Whether time is currently "flowing" forward for this instance.
Returns
-------
None
'''
params = init_preference_shocks.copy()
params.update(kwds)
IndShockConsumerType.__init__(self,
cycles=cycles,
time_flow=time_flow,
**params)
self.solveOnePeriod = solveConsPrefShock # Choose correct solver
def update_solution_terminal(self):
"""
Update the terminal period solution. This method should be run when a
new AgentType is created or when CRRA changes.
Parameters
----------
none
Returns
-------
none
"""
IndShockConsumerType.update_solution_terminal(self)
# Make replicated terminal period solution: consume all resources, no human wealth, minimum m is 0
StateCount = self.MrkvArray[0].shape[0]
self.solution_terminal.cFunc = StateCount * [self.cFunc_terminal_]
self.solution_terminal.vFunc = StateCount * [self.solution_terminal.vFunc]
self.solution_terminal.vPfunc = StateCount * [self.solution_terminal.vPfunc]
self.solution_terminal.vPPfunc = StateCount * [self.solution_terminal.vPPfunc]
self.solution_terminal.mNrmMin = np.zeros(StateCount)
self.solution_terminal.hRto = np.zeros(StateCount)
self.solution_terminal.MPCmax = np.ones(StateCount)
self.solution_terminal.MPCmin = np.ones(StateCount)
def __init__(self, cycles=1, time_flow=True, **kwds):
'''
Instantiate a new ConsumerType with given data, and construct objects
to be used during solution (income distribution, assets grid, etc).
See ConsumerParameters.init_kinky_pref for a dictionary of the keywords
that should be passed to the constructor.
Parameters
----------
cycles : int
Number of times the sequence of periods should be solved.
time_flow : boolean
Whether time is currently "flowing" forward for this instance.
Returns
-------
None
'''
params = Params.init_kinky_pref.copy()
params.update(kwds)
kwds = params
IndShockConsumerType.__init__(self, **kwds)
self.solveOnePeriod = solveConsKinkyPref # Choose correct solver
self.addToTimeInv('Rboro', 'Rsave')
self.delFromTimeInv('Rfree')
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 __init__(self, cycles=1, time_flow=True, **kwds):
'''
Instantiate a new ConsumerType with given data.
See ConsumerParameters.init_idiosyncratic_shocks for a dictionary of
the keywords that should be passed to the constructor.
Parameters
----------
cycles : int
Number of times the sequence of periods should be solved.
time_flow : boolean
Whether time is currently "flowing" forward for this instance.
Returns
-------
None
'''
# Initialize a basic AgentType
IndShockConsumerType.__init__(self,
cycles=cycles,
time_flow=time_flow,
**kwds)
# Add consumer-type specific objects, copying to create independent versions
self.solveOnePeriod = solvePrefLaborShock # idiosyncratic shocks solver
self.update() # Make assets grid, income process, terminal solution
def __init__(self, **kwds):
params = init_kinky_pref.copy()
params.update(kwds)
kwds = params
IndShockConsumerType.__init__(self, **kwds)
self.solve_one_period = make_one_period_oo_solver(ConsKinkyPrefSolver)
self.add_to_time_inv("Rboro", "Rsave")
self.del_from_time_inv("Rfree")
def __init__(self, **kwds):
params = init_rep_agent.copy()
params.update(kwds)
IndShockConsumerType.__init__(self, cycles=0, **params)
self.AgentCount = 1 # Hardcoded, because this is rep agent
self.solve_one_period = solve_ConsRepAgent
self.del_from_time_inv("Rfree", "BoroCnstArt", "vFuncBool", "CubicBool")
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 __init__(self, **kwds):
params = init_labor_intensive.copy()
params.update(kwds)
IndShockConsumerType.__init__(self, **params)
self.pseudo_terminal = False
self.solve_one_period = solve_ConsLaborIntMarg
self.update()
def __init__(self, **kwargs):
IndShockConsumerType.__init__(self, **kwargs)
# Add consumer-type specific objects, copying to create independent versions
if (not self.CubicBool) and (not self.vFuncBool):
solver = ConsIndShockSolverBasicFast
else: # Use the "advanced" solver if either is requested
solver = ConsIndShockSolverFast
self.solve_one_period = make_one_period_oo_solver(solver)
def initialize_sim(self):
self.shocks["Mrkv"] = np.zeros(self.AgentCount, dtype=int)
IndShockConsumerType.initialize_sim(self)
if (self.global_markov
): # Need to initialize markov state to be the same for all agents
base_draw = Uniform(seed=self.RNG.randint(0, 2**31 - 1)).draw(1)
Cutoffs = np.cumsum(np.array(self.MrkvPrbsInit))
self.shocks["Mrkv"] = np.ones(self.AgentCount) * np.searchsorted(
Cutoffs, base_draw).astype(int)
self.shocks["Mrkv"] = self.shocks["Mrkv"].astype(int)
def setUp(self):
"""
Prepare to compare the models by initializing and solving them
"""
# Set up and solve infinite type
# Define a test dictionary that should have the same solution in the
# perfect foresight and idiosyncratic shocks models.
test_dictionary = deepcopy(init_idiosyncratic_shocks)
test_dictionary["LivPrb"] = [1.0]
test_dictionary["DiscFac"] = 0.955
test_dictionary["PermGroFac"] = [1.0]
test_dictionary["PermShkStd"] = [0.0]
test_dictionary["TranShkStd"] = [0.0]
test_dictionary["UnempPrb"] = 0.0
test_dictionary["T_cycle"] = 1
test_dictionary["T_retire"] = 0
test_dictionary["BoroCnstArt"] = None
InfiniteType = IndShockConsumerType(**test_dictionary)
InfiniteType.cycles = 0
InfiniteType.update_income_process()
InfiniteType.solve()
InfiniteType.unpack("cFunc")
# Make and solve a perfect foresight consumer type with the same parameters
PerfectForesightType = PerfForesightConsumerType(**test_dictionary)
PerfectForesightType.cycles = 0
PerfectForesightType.solve()
PerfectForesightType.unpack("cFunc")
self.InfiniteType = InfiniteType
self.PerfectForesightType = PerfectForesightType
def __init__(self, verbose=False, quiet=False, **kwds):
params = init_risky_asset.copy()
params.update(kwds)
kwds = params
# Initialize a basic consumer type
IndShockConsumerType.__init__(self, verbose=verbose, quiet=quiet, **kwds)
# These method must be overwritten by classes that inherit from
# RiskyAssetConsumerType
self.solve_one_period = NullFunc()
def test_lifecyle(self):
LifecycleExample = IndShockConsumerType(**LifecycleDict)
LifecycleExample.cycles = 1
LifecycleExample.solve()
self.assertEquals(len(LifecycleExample.solution), 11)
mMin = np.min([LifecycleExample.solution[t].mNrmMin for t in
range(LifecycleExample.T_cycle)])
self.assertAlmostEqual(LifecycleExample.solution[5].cFunc(3).tolist(),
2.129983771775666)
def read_shocks_from_history(self):
"""
A slight modification of AgentType.read_shocks that makes sure that MrkvNow is int, not float.
Parameters
----------
None
Returns
-------
None
"""
IndShockConsumerType.read_shocks_from_history(self)
self.shocks['Mrkv'] = self.shocks['Mrkv'].astype(int)
def readShocks(self):
'''
A slight modification of AgentType.readShocks that makes sure that MrkvNow is int, not float.
Parameters
----------
None
Returns
-------
None
'''
IndShockConsumerType.readShocks(self)
self.MrkvNow = self.MrkvNow.astype(int)
def test_infinite_horizon(self):
baseEx_inf = IndShockConsumerType(**self.base_params)
baseEx_inf.assign_parameters(cycles=0)
baseEx_inf.solve()
baseEx_inf.unpack("cFunc")
m1 = np.linspace(
1, baseEx_inf.solution[0].mNrmStE, 50
) # m1 defines the plot range on the left of target m value (e.g. m <= target m)
c_m1 = baseEx_inf.cFunc[0](m1)
self.assertAlmostEqual(c_m1[0], 0.8527887545025995)
self.assertAlmostEqual(c_m1[-1], 1.0036279936408656)
x1 = np.linspace(0, 25, 1000)
cfunc_m = baseEx_inf.cFunc[0](x1)
self.assertAlmostEqual(cfunc_m[500], 1.8902146173138235)
self.assertAlmostEqual(cfunc_m[700], 2.1591451850267176)
m = np.linspace(0.001, 8, 1000)
# Use the HARK method derivative to get the derivative of cFunc, and the values are just the MPC
MPC = baseEx_inf.cFunc[0].derivative(m)
self.assertAlmostEqual(MPC[500], 0.08415000641504392)
self.assertAlmostEqual(MPC[700], 0.07173144137912524)
def __init__(self, cycles=1, verbose=False, quiet=False, **kwds):
params = init_portfolio.copy()
params.update(kwds)
kwds = params
# Initialize a basic consumer type
IndShockConsumerType.__init__(
self, cycles=cycles, verbose=verbose, quiet=quiet, **kwds
)
shock_vars = ["PermShkNow", "TranShkNow","AdjustNow","RiskyNow"]
# Set the solver for the portfolio model, and update various constructed attributes
self.solveOnePeriod = solveConsPortfolio
self.update()
def initializeSim(self):
'''
Initialize the state of simulation attributes. Simply calls the same method
for IndShockConsumerType, then sets the type of AdjustNow to bool.
Parameters
----------
None
Returns
-------
None
'''
IndShockConsumerType.initializeSim(self)
self.AdjustNow = self.AdjustNow.astype(bool)
def __init__(self, **kwds):
IndShockConsumerType.__init__(self, **kwds)
self.time_inv = ['aXtraGrid', 'mGrid', 'EGMVector', 'par', 'Util', 'UtilP',
'UtilP_inv', 'saveCommon']
self.par = RetiringDeatonParameters(self.DiscFac, self.CRRA, self.DisUtil, self.Rfree, YRet, YWork, self.sigma)
# d == 2 is working
# - 10.0 moves curve down to improve linear interpolation
self.Util = lambda c, d: utility(c, CRRA) - self.par.DisUtil*(d-1) - 10.0
self.UtilP = lambda c, d: utilityP(c, CRRA) # we require CRRA 1.0 for now...
self.UtilP_inv = lambda u, d: utilityP_inv(u, CRRA) # ... so ...
self.preSolve = self.updateLast
self.solveOnePeriod = solveRetiringDeaton
def test_IndShockConsumerType(self):
try:
model = IndShockConsumerType(**init_lifecycle)
except:
self.fail(
"IndShockConsumerType failed to initialize with Params.init_lifecycle."
)
def test_infinite_horizon(self):
IndShockExample = IndShockConsumerType(**IdiosyncDict)
IndShockExample.cycles = 0 # Make this type have an infinite horizon
IndShockExample.solve()
self.assertAlmostEqual(IndShockExample.solution[0].mNrmSS,
1.5488165705077026)
self.assertAlmostEqual(IndShockExample.solution[0].cFunc.functions[0].x_list[0],
-0.25017509)
IndShockExample.track_vars = ['aNrmNow','mNrmNow','cNrmNow','pLvlNow']
IndShockExample.initializeSim()
IndShockExample.simulate()
self.assertAlmostEqual(IndShockExample.mNrmNow_hist[0][0],
1.0170176090252379)
def update(self):
"""
Update the income process, the assets grid, the persistent income grid,
and the terminal solution.
Parameters
----------
None
Returns
-------
None
"""
IndShockConsumerType.update(self)
self.update_pLvlNextFunc()
self.update_pLvlGrid()
def __init__(self, **kwds):
params = init_explicit_perm_inc.copy()
params.update(kwds)
# Initialize a basic ConsumerType
IndShockConsumerType.__init__(self, **params)
self.solve_one_period = make_one_period_oo_solver(
ConsGenIncProcessSolver)
# a poststate?
self.state_now['aLvl'] = None
self.state_prev['aLvl'] = None
# better way to do this...
self.state_now["mLvl"] = None
self.state_prev["mLvl"] = None