def updateIncomeProcessAlt(self):
tax_rate = (self.income_unemploy*self.p_unemploy)/(self.l_bar*(1.0-self.p_unemploy))
xi_dist = deepcopy(calculateMeanOneLognormalDiscreteApprox(self.xi_N,self.xi_sigma[0]))
xi_dist[0] = np.insert(xi_dist[0]*(1.0-self.p_unemploy),0,self.p_unemploy)
xi_dist[1] = np.insert(self.l_bar*xi_dist[1]*(1.0-tax_rate),0,self.income_unemploy)
psi_dist = calculateMeanOneLognormalDiscreteApprox(self.psi_N,self.psi_sigma[0])
self.income_distrib = [createFlatStateSpaceFromIndepDiscreteProbs(psi_dist,xi_dist)]
if self.income_unemploy > 0:
self.p_zero_income = [0.0]
else:
self.p_zero_income = [self.p_unemploy]
if not 'income_distrib' in self.time_vary:
self.time_vary.append('income_distrib')
if not 'p_zero_income' in self.time_vary:
self.time_vary.append('p_zero_income')
def updateIncomeProcess(self):
'''
Updates this agent's income process based on his own attributes. The
function that generates the discrete income process can be swapped out
for a different process.
'''
original_time = self.time_flow
self.timeFwd()
perm_shocks = calculateMeanOneLognormalDiscreteApprox(self.psi_N, self.psi_sigma)
temp_shocks = addDiscreteOutcomeConstantMean(calculateMeanOneLognormalDiscreteApprox(self.psi_N, self.xi_sigma),self.income_unemploy,self.p_unemploy)
self.income_distrib = createFlatStateSpaceFromIndepDiscreteProbs(perm_shocks,temp_shocks)
self.p_zero_income = np.sum(temp_shocks[0][temp_shocks[1] == 0])
if not 'income_distrib' in self.time_vary:
self.time_vary.append('income_distrib')
if not 'p_zero_income' in self.time_vary:
self.time_vary.append('p_zero_income')
if not original_time:
self.timeRev()
def constructLognormalIncomeProcessUnemploymentFailure(parameters):
"""
Generates a list of discrete approximations to the income process for each
life period, from end of life to beginning of life. The process is identical
to constructLognormalIncomeProcessUnemployment but for a very tiny possibility
that unemployment benefits are not provided.
Parameters:
-----------
psi_sigma: [float]
Array of standard deviations in _permanent_ income uncertainty during
the agent's life.
psi_N: int
The number of approximation points to be used in the equiprobable
discrete approximation to the permanent income shock distribution.
xi_sigma [float]
Array of standard deviations in _temporary_ income uncertainty during
the agent's life.
xi_N: int
The number of approximation points to be used in the equiprobable
discrete approximation to the permanent income shock distribution.
p_unemploy: float
The probability of becoming unemployed
p_unemploy_retire: float
The probability of not receiving typical retirement income in any retired period
T_retire: int
The index value i equal to the final working period in the agent's life.
If final_work_index < 0 then there is no retirement.
income_unemploy: float
Income received when unemployed. Often zero.
income_unemploy_retire: float
Income received while "unemployed" when retired. Often zero.
T_total: int
Total number of non-terminal periods in this consumer's life.
Returns
=======
income_distrib: [income distribution]
Each element contains the joint distribution of permanent and transitory
income shocks, as a set of vectors: psi_shock, xi_shock, and pmf. The
first two are the points in the joint state space, and final vector is
the joint pmf over those points. For example,
psi_shock[20], xi_shock[20], and pmf[20]
refers to the (psi, xi) point indexed by 20, with probability p = pmf[20].
p_zero_income: [float]
A list of probabilities of receiving exactly zero income in each period.
"""
# Unpack the parameters from the input
psi_sigma = parameters.psi_sigma
psi_N = parameters.psi_N
xi_sigma = parameters.xi_sigma
xi_N = parameters.xi_N
T_total = parameters.T_total
p_unemploy = parameters.p_unemploy
p_unemploy_retire = parameters.p_unemploy_retire
T_retire = parameters.T_retire
income_unemploy = parameters.income_unemploy
income_unemploy_retire = parameters.income_unemploy_retire
# Set a small possibility of unemployment benefit failure
p_fail = 0.01
income_distrib = [] # Discrete approximation to income process
p_zero_income = [] # Probability of zero income in each period of life
# Fill out a simple discrete RV for retirement, with value 1.0 (mean of shocks)
# in normal times; value 0.0 in "unemployment" times with small prob.
if T_retire >= 0:
if p_unemploy_retire > 0:
retire_perm_income_values = np.array(
[1.0, 1.0]
) # Permanent income is deterministic in retirement (2 states for temp income shocks)
retire_income_values = np.array([
income_unemploy_retire,
(1.0 - p_unemploy_retire * income_unemploy_retire) /
(1.0 - p_unemploy_retire)
])
retire_income_probs = np.array(
[p_unemploy_retire, 1.0 - p_unemploy_retire])
if income_unemploy_retire > 0:
temp_dist = addDiscreteOutcomeConstantMean(
[retire_income_values, retire_income_probs],
p=(p_fail * p_unemploy_retire),
x=0)
retire_income_values = temp_dist[0]
retire_income_probs = temp_dist[1]
retire_perm_income_values = np.array([1.0, 1.0, 1.0])
else:
retire_perm_income_values = np.array([1.0])
retire_income_values = np.array([1.0])
retire_income_probs = np.array([1.0])
income_dist_retire = [
retire_perm_income_values, retire_income_values,
retire_income_probs
]
# Loop to fill in the list of income_distrib random variables.
for Tminust in range(
T_total
): # Iterate over all periods, counting back from the final period.
if T_retire >= 0 and Tminust < T_retire:
# Then we are in the "retirement period" and add a retirement income object.
income_distrib.append(deepcopy(income_dist_retire))
if income_unemploy_retire == 0:
p_zero_income.append(p_unemploy_retire)
else:
p_zero_income.append(p_fail * p_unemploy_retire)
else:
# We are in the "working life" periods.
temp_xi_dist = calculateMeanOneLognormalDiscreteApprox(
N=xi_N, sigma=xi_sigma[Tminust])
if p_unemploy > 0:
temp_xi_dist = addDiscreteOutcomeConstantMean(
temp_xi_dist, p=p_unemploy, x=income_unemploy)
if income_unemploy > 0:
temp_xi_dist = addDiscreteOutcomeConstantMean(
temp_xi_dist, p=(p_unemploy * p_fail), x=0)
temp_psi_dist = calculateMeanOneLognormalDiscreteApprox(
N=psi_N, sigma=psi_sigma[Tminust])
income_distrib.append(
createFlatStateSpaceFromIndepDiscreteProbs(
temp_psi_dist, temp_xi_dist))
if p_unemploy > 0:
if income_unemploy == 0:
p_zero_income.append(p_unemploy)
else:
p_zero_income.append(p_unemploy * p_fail)
else:
p_zero_income.append(0)
return income_distrib, p_zero_income
