def updateIncomeProcessAlt(self):
'''
An alternative method for constructing the income process in the infinite
horizon model, where the labor supply l_bar creates a small oddity.
Parameters
----------
none
Returns
-------
none
'''
tax_rate = (self.IncUnemp * self.UnempPrb) / (self.l_bar *
(1.0 - self.UnempPrb))
TranShkDstn = deepcopy(
approxMeanOneLognormal(self.TranShkCount,
sigma=self.TranShkStd[0],
tail_N=0))
TranShkDstn[0] = np.insert(TranShkDstn[0] * (1.0 - self.UnempPrb), 0,
self.UnempPrb)
TranShkDstn[1] = np.insert(
self.l_bar * TranShkDstn[1] * (1.0 - tax_rate), 0, self.IncUnemp)
PermShkDstn = approxMeanOneLognormal(self.PermShkCount,
sigma=self.PermShkStd[0],
tail_N=0)
self.IncomeDstn = [combineIndepDstns(PermShkDstn, TranShkDstn)]
self.TranShkDstn = TranShkDstn
self.PermShkDstn = PermShkDstn
self.addToTimeVary('IncomeDstn')
def updateIncomeProcess(self):
'''
An alternative method for constructing the income process in the infinite horizon model.
Parameters
----------
none
Returns
-------
none
'''
if self.cycles == 0:
tax_rate = (self.IncUnemp * self.UnempPrb) / (
(1.0 - self.UnempPrb) * self.IndL)
TranShkDstn = deepcopy(
approxMeanOneLognormal(self.TranShkCount,
sigma=self.TranShkStd[0],
tail_N=0))
TranShkDstn[0] = np.insert(TranShkDstn[0] * (1.0 - self.UnempPrb),
0, self.UnempPrb)
TranShkDstn[1] = np.insert(
TranShkDstn[1] * (1.0 - tax_rate) * self.IndL, 0,
self.IncUnemp)
PermShkDstn = approxMeanOneLognormal(self.PermShkCount,
sigma=self.PermShkStd[0],
tail_N=0)
self.IncomeDstn = [combineIndepDstns(PermShkDstn, TranShkDstn)]
self.TranShkDstn = TranShkDstn
self.PermShkDstn = PermShkDstn
self.addToTimeVary('IncomeDstn')
else: # Do the usual method if this is the lifecycle model
EstimationAgentClass.updateIncomeProcess(self)
def updatePrefShockProcess(self):
'''
Make a discrete preference shock structure for each period in the cycle
for this agent type, storing them as attributes of self for use in the
solution (and other methods).
Parameters
----------
none
Returns
-------
none
'''
time_orig = self.time_flow
self.timeFwd()
PrefShkDstn = [] # discrete distributions of preference shocks
for t in range(len(self.PrefShkStd)):
PrefShkStd = self.PrefShkStd[t]
PrefShkDstn.append(
approxMeanOneLognormal(N=self.PrefShkCount,
sigma=PrefShkStd,
tail_N=self.PrefShk_tail_N))
# Store the preference shocks in self (time-varying) and restore time flow
self.PrefShkDstn = PrefShkDstn
self.addToTimeVary('PrefShkDstn')
if not time_orig:
self.timeRev()
def getShocks(self):
'''
Gets permanent and transitory income shocks for this period as well as preference shocks.
Parameters
----------
None
Returns
-------
None
'''
IndShockConsumerType.getShocks(self) # Get permanent and transitory income shocks
PrefShkNow = np.zeros(self.AgentCount) # Initialize shock array
for t in range(self.T_cycle):
these = t == self.t_cycle
N = np.sum(these)
if N > 0:
PrefShkNow[these] = self.RNG.permutation(approxMeanOneLognormal(N,sigma=self.PrefShkStd[t])[1])
self.PrefShkNow = PrefShkNow
def constructLognormalIncomeAndPreferenceProcess(parameters):
'''
Generates a list of discrete approximations to the income and preference shock
process . Permanent shocks are mean one lognormally distributed
with standard deviation PermShkStd. Transitory shocks
are mean one lognormally distributed
Parameters (passed as attributes of the input parameters)
----------
PermShkStd : [float]
List of standard deviations in log permanent income uncertainty during
the agent's life.
PermShkCount : int
The number of approximation points to be used in the discrete approxima-
tion to the permanent income shock distribution.
TranShkStd : [float]
List of standard deviations in log transitory income uncertainty during
the agent's life.
TranShkCount : int
The number of approximation points to be used in the discrete approxima-
tion to the permanent income shock distribution.
PrefShkStd : [float]
List of standard deviations in log preference uncertainty during
the agent's life.
PrefShkCount : int
The number of approximation points to be used in the discrete approxima-
tion to the preference shock distribution.
Returns
-------
IncomeAndPrefDstn : [[np.array]]
A list with T_cycle elements, each of which is a list of four arrays
representing a discrete approximation to the income and preference
process in a period.
Order: probabilities, permanent shocks, transitory shocks, preference shocks.
PermShkDstn : [[np.array]]
A list with T_cycle elements, each of which is a list of two arrays
representing a discrete approximation to the permanent income shocks.
TranShkDstn : [[np.array]]
A list with T_cycle elements, each of which is a list of two arrays
representing a discrete approximation to the transitory income shocks.
PrefShkDstn : [[np.array]]
A list with T_cycle elements, each of which is a list of two arrays
representing a discrete approximation to the preference shocks.
'''
# Unpack the parameters from the input
PermShkStd = parameters.PermShkStd
PermShkCount = parameters.PermShkCount
TranShkStd = parameters.TranShkStd
TranShkCount = parameters.TranShkCount
PrefShkStd = parameters.PrefShkStd
PrefShkCount = parameters.PrefShkCount
UnempPrb = parameters.UnempPrb
IncUnemp = parameters.IncUnemp
IncomeDstn = [] # Discrete approximations to income process in each period
PermShkDstn = [] # Discrete approximations to permanent income shocks
TranShkDstn = [] # Discrete approximations to transitory income shocks
PrefShkDstn = [] # Discrete approximations to preference shocks
t = 0
TranShkDstn_t = approxMeanOneLognormal(N=TranShkCount,
sigma=TranShkStd[t],
tail_N=0)
if UnempPrb > 0:
TranShkDstn_t = addDiscreteOutcomeConstantMean(TranShkDstn_t,
p=UnempPrb,
x=IncUnemp)
#add in a shock at zero to impose natural borrowing constraint
TranShkDstn_t = addDiscreteOutcomeConstantMean(TranShkDstn_t,
p=0.000000001,
x=0.0)
PermShkDstn_t = approxMeanOneLognormal(N=PermShkCount,
sigma=PermShkStd[t],
tail_N=0)
PrefShkDstn_t = approxMeanOneLognormal(N=PrefShkCount,
sigma=PrefShkStd[t],
tail_N=0)
IncomeDstn.append(
combineIndepDstns(PermShkDstn_t, TranShkDstn_t,
PrefShkDstn_t)) # mix the independent distributions
PermShkDstn.append(PermShkDstn_t)
TranShkDstn.append(TranShkDstn_t)
PrefShkDstn.append(PrefShkDstn_t)
return IncomeDstn, PermShkDstn, TranShkDstn, PrefShkDstn
# is actually the true shock structure. That is, that the aggregate state only changes with prob UpdatePrb,
# but that the state changes are as if ~1/UpdatePrb periods have elapsed.
t_exp_between_updates = int(np.round(1. / UpdatePrb))
PolyMrkvArrayAlt = PolyMrkvArray
for t in range(t_exp_between_updates - 1): # Premultiply T-1 times
PolyMrkvArrayAlt = np.dot(PolyMrkvArray, PolyMrkvArrayAlt)
PolyMrkvArrayAlt *= UpdatePrb # Scale down all transitions so they only happen with UpdatePrb probability
PolyMrkvArrayAlt += (1. - UpdatePrb) * np.eye(
StateCount) # Move that probability weight to no change
# In the alternate specification, agents also think that permanent aggregate shocks only
# happen with UpdatePrb probability, but are 1/UpdatePrb times larger when they do happen.
# Transitory aggregate shocks are interpreted to be much larger when not updating.
PermShkAggVarAlt = PermShkAggVar / UpdatePrb
PermShkAggStdAlt = np.sqrt(PermShkAggVarAlt)
PermShkAggDstnAlt_update = approxMeanOneLognormal(5, PermShkAggStdAlt)
TranShkAggDstnAlt_update = approxMeanOneLognormal(5, np.sqrt(TranShkAggVar))
AggShkDstnAlt_update = combineIndepDstns(PermShkAggDstnAlt_update,
TranShkAggDstnAlt_update)
AggShkDstnAlt_update[0] *= UpdatePrb
PermShkAggDstnAlt_dont = [np.array([1.0]),
np.array([1.0])] # Degenerate distribution
TranShkAggDstnAlt_dont = approxMeanOneLognormal(
5, np.sqrt(TranShkAggVar + PermShkAggVar / UpdatePrb))
AggShkDstnAlt_dont = combineIndepDstns(PermShkAggDstnAlt_dont,
TranShkAggDstnAlt_dont)
AggShkDstnAlt_dont[0] *= 1. - UpdatePrb
AggShkDstnAlt = StateCount * [[
np.concatenate([AggShkDstnAlt_update[n], AggShkDstnAlt_dont[n]])
for n in range(3)
]]
