def getAdjust(self):
'''
Sets the attribute AdjustNow as a boolean array of size AgentCount, indicating
whether each agent is able to adjust their risky portfolio share this period.
Uses the attribute AdjustPrb to draw from a Bernoulli distribution.
Parameters
----------
None
Returns
-------
None
'''
self.AdjustNow = Bernoulli(self.AdjustPrb).draw(self.AgentCount, seed=self.RNG.randint(0, 2**31-1))
def noticeStimulus(self):
'''
Give each agent the opportunity to notice the future stimulus payment and
mentally account for it in their market resources.
'''
if self.T_til_check > 0:
self.T_til_check -= 1
updaters = Bernoulli(p=self.UpdatePrb).draw(self.AgentCount, seed=self.RNG.randint(0,2**31-1))
if self.T_til_check == 0:
updaters = np.ones(self.AgentCount, dtype=bool)
self.mNrmNow[updaters] += self.Stim_unnoticed[updaters]*self.StimLvl[updaters]/self.pLvlNow[updaters]*self.Rfree[0]**(-self.T_til_check)
self.Stim_unnoticed[updaters] = False
def getShocks(self):
'''
Determine which agents switch from employment to unemployment. All unemployed agents remain
unemployed until death.
Parameters
----------
None
Returns
-------
None
'''
employed = self.eStateNow == 1.0
N = int(np.sum(employed))
newly_unemployed = Bernoulli(self.UnempPrb).draw(N,
seed=self.RNG.randint(0,2**31-1))
self.eStateNow[employed] = 1.0 - newly_unemployed
def get_shocks(self):
"""
Determine which agents switch from employment to unemployment. All unemployed agents remain
unemployed until death.
Parameters
----------
None
Returns
-------
None
"""
employed = self.shocks["eStateNow"] == 1.0
N = int(np.sum(employed))
newly_unemployed = Bernoulli(self.UnempPrb,
seed=self.RNG.randint(0,
2**31 - 1)).draw(N)
self.shocks["eStateNow"][employed] = 1.0 - newly_unemployed
class PortfolioConsumerType(IndShockConsumerType):
"""
A consumer type with a portfolio choice. This agent type has log-normal return
factors. Their problem is defined by a coefficient of relative risk aversion,
intertemporal discount factor, risk-free interest factor, and time sequences of
permanent income growth rate, survival probability, and permanent and transitory
income shock standard deviations (in logs). The agent may also invest in a risky
asset, which has a higher average return than the risk-free asset. He *might*
have age-varying beliefs about the risky-return; if he does, then "true" values
of the risky asset's return distribution must also be specified.
"""
poststate_vars_ = ['aNrmNow', 'pLvlNow', 'ShareNow', 'AdjustNow']
time_inv_ = deepcopy(IndShockConsumerType.time_inv_)
time_inv_ = time_inv_ + ['AdjustPrb', 'DiscreteShareBool']
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
)
# Set the solver for the portfolio model, and update various constructed attributes
self.solveOnePeriod = solveConsPortfolio
self.update()
def preSolve(self):
AgentType.preSolve(self)
self.updateSolutionTerminal()
def update(self):
IndShockConsumerType.update(self)
self.updateRiskyDstn()
self.updateShockDstn()
self.updateShareGrid()
self.updateShareLimit()
def updateSolutionTerminal(self):
'''
Solves the terminal period of the portfolio choice problem. The solution is
trivial, as usual: consume all market resources, and put nothing in the risky
asset (because you have nothing anyway).
Parameters
----------
None
Returns
-------
None
'''
# Consume all market resources: c_T = m_T
cFuncAdj_terminal = IdentityFunction()
cFuncFxd_terminal = IdentityFunction(i_dim=0, n_dims=2)
# Risky share is irrelevant-- no end-of-period assets; set to zero
ShareFuncAdj_terminal = ConstantFunction(0.)
ShareFuncFxd_terminal = IdentityFunction(i_dim=1, n_dims=2)
# Value function is simply utility from consuming market resources
vFuncAdj_terminal = ValueFunc(cFuncAdj_terminal, self.CRRA)
vFuncFxd_terminal = ValueFunc2D(cFuncFxd_terminal, self.CRRA)
# Marginal value of market resources is marg utility at the consumption function
vPfuncAdj_terminal = MargValueFunc(cFuncAdj_terminal, self.CRRA)
dvdmFuncFxd_terminal = MargValueFunc2D(cFuncFxd_terminal, self.CRRA)
dvdsFuncFxd_terminal = ConstantFunction(0.) # No future, no marg value of Share
# Construct the terminal period solution
self.solution_terminal = PortfolioSolution(
cFuncAdj=cFuncAdj_terminal,
ShareFuncAdj=ShareFuncAdj_terminal,
vFuncAdj=vFuncAdj_terminal,
vPfuncAdj=vPfuncAdj_terminal,
cFuncFxd=cFuncFxd_terminal,
ShareFuncFxd=ShareFuncFxd_terminal,
vFuncFxd=vFuncFxd_terminal,
dvdmFuncFxd=dvdmFuncFxd_terminal,
dvdsFuncFxd=dvdsFuncFxd_terminal
)
def updateRiskyDstn(self):
'''
Creates the attributes RiskyDstn from the primitive attributes RiskyAvg,
RiskyStd, and RiskyCount, approximating the (perceived) distribution of
returns in each period of the cycle.
Parameters
----------
None
Returns
-------
None
'''
# Determine whether this instance has time-varying risk perceptions
if (type(self.RiskyAvg) is list) and (type(self.RiskyStd) is list) and (len(self.RiskyAvg) == len(self.RiskyStd)) and (len(self.RiskyAvg) == self.T_cycle):
self.addToTimeVary('RiskyAvg','RiskyStd')
elif (type(self.RiskyStd) is list) or (type(self.RiskyAvg) is list):
raise AttributeError('If RiskyAvg is time-varying, then RiskyStd must be as well, and they must both have length of T_cycle!')
else:
self.addToTimeInv('RiskyAvg','RiskyStd')
# Generate a discrete approximation to the risky return distribution if the
# agent has age-varying beliefs about the risky asset
if 'RiskyAvg' in self.time_vary:
RiskyDstn = []
for t in range(self.T_cycle):
RiskyAvgSqrd = self.RiskyAvg[t] ** 2
RiskyVar = self.RiskyStd[t] ** 2
mu = np.log(self.RiskyAvg[t] / (np.sqrt(1. + RiskyVar / RiskyAvgSqrd)))
sigma = np.sqrt(np.log(1. + RiskyVar / RiskyAvgSqrd))
RiskyDstn.append(approxLognormal(self.RiskyCount, mu=mu, sigma=sigma))
self.RiskyDstn = RiskyDstn
self.addToTimeVary('RiskyDstn')
# Generate a discrete approximation to the risky return distribution if the
# agent does *not* have age-varying beliefs about the risky asset (base case)
else:
RiskyAvgSqrd = self.RiskyAvg ** 2
RiskyVar = self.RiskyStd ** 2
mu = np.log(self.RiskyAvg / (np.sqrt(1. + RiskyVar / RiskyAvgSqrd)))
sigma = np.sqrt(np.log(1. + RiskyVar / RiskyAvgSqrd))
self.RiskyDstn = approxLognormal(self.RiskyCount, mu=mu, sigma=sigma)
self.addToTimeInv('RiskyDstn')
def updateShockDstn(self):
'''
Combine the income shock distribution (over PermShk and TranShk) with the
risky return distribution (RiskyDstn) to make a new attribute called ShockDstn.
Parameters
----------
None
Returns
-------
None
'''
if 'RiskyDstn' in self.time_vary:
self.ShockDstn = [combineIndepDstns(self.IncomeDstn[t], self.RiskyDstn[t]) for t in range(self.T_cycle)]
else:
self.ShockDstn = [combineIndepDstns(self.IncomeDstn[t], self.RiskyDstn) for t in range(self.T_cycle)]
self.addToTimeVary('ShockDstn')
# Mark whether the risky returns and income shocks are independent (they are)
self.IndepDstnBool = True
self.addToTimeInv('IndepDstnBool')
def updateShareGrid(self):
'''
Creates the attribute ShareGrid as an evenly spaced grid on [0.,1.], using
the primitive parameter ShareCount.
Parameters
----------
None
Returns
-------
None
'''
self.ShareGrid = np.linspace(0.,1.,self.ShareCount)
self.addToTimeInv('ShareGrid')
def updateShareLimit(self):
'''
Creates the attribute ShareLimit, representing the limiting lower bound of
risky portfolio share as mNrm goes to infinity.
Parameters
----------
None
Returns
-------
None
'''
if 'RiskyDstn' in self.time_vary:
self.ShareLimit = []
for t in range(self.T_cycle):
RiskyDstn = self.RiskyDstn[t]
temp_f = lambda s : -((1.-self.CRRA)**-1)*np.dot((self.Rfree + s*(RiskyDstn.X-self.Rfree))**(1.-self.CRRA), RiskyDstn.pmf)
SharePF = minimize_scalar(temp_f, bounds=(0.0, 1.0), method='bounded').x
self.ShareLimit.append(SharePF)
self.addToTimeVary('ShareLimit')
else:
RiskyDstn = self.RiskyDstn
temp_f = lambda s : -((1.-self.CRRA)**-1)*np.dot((self.Rfree + s*(RiskyDstn.X-self.Rfree))**(1.-self.CRRA), RiskyDstn.pmf)
SharePF = minimize_scalar(temp_f, bounds=(0.0, 1.0), method='bounded').x
self.ShareLimit = SharePF
self.addToTimeInv('ShareLimit')
def getRisky(self):
'''
Sets the attribute RiskyNow as a single draw from a lognormal distribution.
Uses the attributes RiskyAvgTrue and RiskyStdTrue if RiskyAvg is time-varying,
else just uses the single values from RiskyAvg and RiskyStd.
Parameters
----------
None
Returns
-------
None
'''
if 'RiskyDstn' in self.time_vary:
RiskyAvg = self.RiskyAvgTrue
RiskyStd = self.RiskyStdTrue
else:
RiskyAvg = self.RiskyAvg
RiskyStd = self.RiskyStd
RiskyAvgSqrd = RiskyAvg**2
RiskyVar = RiskyStd**2
mu = np.log(RiskyAvg / (np.sqrt(1. + RiskyVar / RiskyAvgSqrd)))
sigma = np.sqrt(np.log(1. + RiskyVar / RiskyAvgSqrd))
self.RiskyNow = Lognormal(mu, sigma).draw(1, seed=self.RNG.randint(0, 2**31-1))
def getAdjust(self):
'''
Sets the attribute AdjustNow as a boolean array of size AgentCount, indicating
whether each agent is able to adjust their risky portfolio share this period.
Uses the attribute AdjustPrb to draw from a Bernoulli distribution.
Parameters
----------
None
Returns
-------
None
'''
self.AdjustNow = Bernoulli(self.AdjustPrb).draw(self.AgentCount, seed=self.RNG.randint(0, 2**31-1))
def getRfree(self):
'''
Calculates realized return factor for each agent, using the attributes Rfree,
RiskyNow, and ShareNow. This method is a bit of a misnomer, as the return
factor is not riskless, but would more accurately be labeled as Rport. However,
this method makes the portfolio model compatible with its parent class.
Parameters
----------
None
Returns
-------
Rport : np.array
Array of size AgentCount with each simulated agent's realized portfolio
return factor. Will be used by getStates() to calculate mNrmNow, where it
will be mislabeled as "Rfree".
'''
Rport = self.ShareNow*self.RiskyNow + (1.-self.ShareNow)*self.Rfree
self.RportNow = Rport
return Rport
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 simBirth(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.simBirth(self,which_agents)
self.ShareNow[which_agents] = 0.
self.AdjustNow[which_agents] = False
def getShocks(self):
'''
Draw idiosyncratic income shocks, just as for IndShockConsumerType, then draw
a single common value for the risky asset return. Also draws whether each
agent is able to update their risky asset share this period.
Parameters
----------
None
Returns
-------
None
'''
IndShockConsumerType.getShocks(self)
self.getRisky()
self.getAdjust()
def getControls(self):
'''
Calculates consumption cNrmNow and risky portfolio share ShareNow using
the policy functions in the attribute solution. These are stored as attributes.
Parameters
----------
None
Returns
-------
None
'''
cNrmNow = np.zeros(self.AgentCount) + np.nan
ShareNow = np.zeros(self.AgentCount) + np.nan
# Loop over each period of the cycle, getting controls separately depending on "age"
for t in range(self.T_cycle):
these = t == self.t_cycle
# Get controls for agents who *can* adjust their portfolio share
those = np.logical_and(these, self.AdjustNow)
cNrmNow[those] = self.solution[t].cFuncAdj(self.mNrmNow[those])
ShareNow[those] = self.solution[t].ShareFuncAdj(self.mNrmNow[those])
# Get Controls for agents who *can't* adjust their portfolio share
those = np.logical_and(these, np.logical_not(self.AdjustNow))
cNrmNow[those] = self.solution[t].cFuncFxd(self.mNrmNow[those], self.ShareNow[those])
ShareNow[those] = self.solution[t].ShareFuncFxd(self.mNrmNow[those], self.ShareNow[those])
# Store controls as attributes of self
self.cNrmNow = cNrmNow
self.ShareNow = ShareNow
def test_Bernoulli(self):
self.assertEqual(Bernoulli().draw(1)[0], False)