class PortfolioConsumerFrameType(FrameAgentType, PortfolioConsumerType):
"""
A consumer type with a portfolio choice, using Frame architecture.
A subclass of PortfolioConsumerType for now.
This is mainly to keep the _solver_ logic intact.
"""
def __init__(self, **kwds):
params = init_portfolio.copy()
params.update(kwds)
kwds = params
# Initialize a basic consumer type
PortfolioConsumerType.__init__(self, **kwds)
# Initialize a basic consumer type
FrameAgentType.__init__(self, self.model, **kwds)
self.shocks = {}
self.controls = {}
self.state_now = {}
def solve(self):
# Some contortions are needed here to make decision rule shaped objects
# out of the HARK solution objects
super().solve(self)
## TODO: make this a property of FrameAgentTypes or FrameModels?
self.decision_rules = {}
def decision_rule_Share_from_solution(solution_t):
def decision_rule_Share(Adjust, mNrm, Share):
Share = np.zeros(len(Adjust)) + np.nan
Share[Adjust] = solution_t.ShareFuncAdj(mNrm[Adjust])
Share[~Adjust] = solution_t.ShareFuncFxd(
mNrm[~Adjust], Share[~Adjust])
return Share
return decision_rule_Share
def decision_rule_cNrm_from_solution(solution_t):
def decision_rule_cNrm(Adjust, mNrm, Share):
cNrm = np.zeros(len(Adjust)) + np.nan
cNrm[Adjust] = solution_t.cFuncAdj(mNrm[Adjust])
cNrm[~Adjust] = solution_t.cFuncFxd(mNrm[~Adjust],
Share[~Adjust])
return cNrm
return decision_rule_cNrm
self.decision_rules[('Share', )] = [
decision_rule_Share_from_solution(sol) for sol in self.solution
]
self.decision_rules[('cNrm', )] = [
decision_rule_cNrm_from_solution(sol) for sol in self.solution
]
# TODO: streamline this so it can draw the parameters from context
def birth_aNrmNow(self, N):
"""
Birth value for aNrmNow
"""
return Lognormal(
mu=self.aNrmInitMean,
sigma=self.aNrmInitStd,
seed=self.RNG.randint(0, 2**31 - 1),
).draw(N)
# TODO: streamline this so it can draw the parameters from context
def birth_pLvlNow(self, N):
"""
Birth value for pLvlNow
"""
pLvlInitMeanNow = self.pLvlInitMean + np.log(
self.state_now["PlvlAgg"]
) # Account for newer cohorts having higher permanent income
return Lognormal(pLvlInitMeanNow,
self.pLvlInitStd,
seed=self.RNG.randint(0, 2**31 - 1)).draw(N)
# maybe replace reference to init_portfolio to self.parameters?
model = FrameModel(
[
# todo : make an aggegrate value
Frame(('PermShkAgg', ), ('PermGroFacAgg', ),
transition=lambda PermGroFacAgg: (PermGroFacAgg, ),
aggregate=True),
Frame(
('PermShk'),
None,
default={
'PermShk': 1.0
}, # maybe this is unnecessary because the shock gets sampled at t = 0
# this is discretized before it's sampled
transition=IndexDistribution(
Lognormal.from_mean_std, {
'mean': init_portfolio['PermGroFac'],
'std': init_portfolio['PermShkStd']
}).approx(init_portfolio['PermShkCount'], tail_N=0),
),
Frame(
('TranShk'),
None,
default={
'TranShk': 1.0
}, # maybe this is unnecessary because the shock gets sampled at t = 0
transition=add_discrete_outcome_constant_mean(
IndexDistribution(MeanOneLogNormal, {
'sigma': init_portfolio['TranShkStd']
}).approx(init_portfolio['TranShkCount'], tail_N=0),
p=init_portfolio['UnempPrb'],
x=init_portfolio['IncUnemp'])),
Frame( ## TODO: Handle Risky as an Aggregate value
('Risky'),
None,
transition=IndexDistribution(
Lognormal.from_mean_std, {
'mean': init_portfolio['RiskyAvg'],
'std': init_portfolio['RiskyStd']
}
# seed=self.RNG.randint(0, 2 ** 31 - 1) : TODO: Seed logic
).approx(init_portfolio['RiskyCount']),
aggregate=True),
Frame(
('Adjust'),
None,
default={'Adjust': False},
transition=IndexDistribution(
Bernoulli,
{'p': init_portfolio['AdjustPrb']},
# seed=self.RNG.randint(0, 2 ** 31 - 1) : TODO: Seed logic
) # self.t_cycle input implied
),
Frame(('Rport'), ('Share', 'Risky', 'Rfree'),
transition=lambda Share, Risky, Rfree:
(Share * Risky + (1.0 - Share) * Rfree, )),
Frame(('PlvlAgg'), ('PlvlAgg', 'PermShkAgg'),
default={'PlvlAgg': 1.0},
transition=lambda PlvlAgg, PermShkAgg: PlvlAgg * PermShkAgg,
aggregate=True),
Frame(('pLvl', ), ('pLvl', 'PermShk'),
default={'pLvl': birth_pLvlNow},
transition=lambda pLvl, PermShk: (pLvl * PermShk, )),
Frame(('bNrm', ), ('aNrm', 'Rport', 'PermShk'),
transition=lambda aNrm, Rport, PermShk:
(Rport / PermShk) * aNrm),
Frame(('mNrm', ), ('bNrm', 'TranShk'),
transition=lambda bNrm, TranShk: (bNrm + TranShk, )),
Frame(('Share'), ('Adjust', 'mNrm', 'Share'),
default={'Share': 0},
control=True),
Frame(('cNrm'), ('Adjust', 'mNrm', 'Share'), control=True),
Frame(
('U'),
('cNrm',
'CRRA'), ## Note CRRA here is a parameter not a state var
transition=lambda cNrm, CRRA: (CRRAutility(cNrm, CRRA), ),
reward=True),
Frame(('aNrm'), ('mNrm', 'cNrm'),
default={'aNrm': birth_aNrmNow},
transition=lambda mNrm, cNrm: (mNrm - cNrm, )),
Frame(('aLvl'), ('aNrm', 'pLvl'),
transition=lambda aNrm, pLvl: (aNrm * pLvl, ))
],
init_portfolio)
aGrid = np.linspace(0,8,400) # Savings grid for EGM. # Model parameters # Parameters that need to be fixed # Relative risk aversion. This is fixed at 2 in order to mantain # the analytical solution that we use, from Carroll (2000) CRRA = 2 # Parameters that can be changed. w = 1 # Deterministic wage per period. willCstFac = 0.35 # Fraction of resources charged by lawyer for writing a will. DiscFac = 0.98 # Time-discount factor. # Define utility (and related) functions u = lambda x: CRRAutility(x,CRRA) uP = lambda x: CRRAutilityP(x, CRRA) uPinv = lambda x: CRRAutilityP_inv(x, CRRA) # Create a grid for market resources mGrid = (aGrid-aGrid[0])*1.5 mGridPlots = np.linspace(w,10*w,100) mGridPlotsC = np.insert(mGridPlots,0,0) # Transformations for value funtion interpolation vTransf = lambda x: np.exp(x) vUntransf = lambda x: np.log(x) # %% [markdown] # # The third (last) period of life #
init_parameters['PermShkCount'] = 5
init_parameters['TranShkStd'] = 3.0
init_parameters['TranShkCount'] = 5
init_parameters['RiskyAvg'] = 1.05
init_parameters['RiskyStd'] = 1.5
init_parameters['RiskyCount'] = 5
init_parameters['Rfree'] = 1.03
frames_A = [
Frame(('bNrm', ), ('aNrm', ), transition=lambda Rfree, aNrm: Rfree * aNrm),
Frame(('mNrm', ), ('bNrm', 'TranShk'), transition=lambda bNrm: mNrm),
Frame(('cNrm'), ('mNrm', ), control=True),
Frame(
('U'),
('cNrm', 'CRRA'), ## Note CRRA here is a parameter not a state var
transition=lambda cNrm, CRRA: (CRRAutility(cNrm, CRRA), ),
reward=True,
context={'CRRA': 2.0}),
Frame(('aNrm'), ('mNrm', 'cNrm'),
transition=lambda mNrm, cNrm: (mNrm - cNrm, )),
]
class test_FrameModel(unittest.TestCase):
def setUp(self):
self.model = FrameModel(frames_A, init_parameters)
def test_init(self):
self.model.frames.var('aNrm')