def simMortality(self):
'''
Simulates the mortality process, killing off some percentage of agents
and replacing them with newborn agents.
Parameters
----------
none
Returns
-------
none
'''
if hasattr(self, 'DiePrb'):
if self.DiePrb > 0:
who_dies = drawBernoulli(N=self.Nagents,
p=self.DiePrb,
seed=self.RNG.randint(low=1,
high=2**31 - 1))
wealth_all = self.aNow * self.pNow
who_lives = np.logical_not(who_dies)
wealth_of_dead = np.sum(wealth_all[who_dies])
wealth_of_live = np.sum(wealth_all[who_lives])
R_actuarial = 1.0 + wealth_of_dead / wealth_of_live
self.aNow[who_dies] = 0.0
self.pNow[who_dies] = 1.0
self.aNow = self.aNow * R_actuarial
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 = drawBernoulli(N,p=self.UnempPrb,seed=self.RNG.randint(0,2**31-1))
self.eStateNow[employed] = 1.0 - newly_unemployed
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 = drawBernoulli(N,p=self.UnempPrb,seed=self.RNG.randint(0,2**31-1))
self.eStateNow[employed] = 1.0 - newly_unemployed
def makeIncShkHist(self):
'''
Makes histories of simulated income shocks for this consumer type by
drawing from the discrete income distributions, storing them as attributes
of self for use by simulation methods.
Parameters
----------
None
Returns
-------
None
'''
# After running the routine for AggShockConsumerType, just add in updateBeliefHist
AggShockConsumerType.makeIncShkHist(self)
orig_time = self.time_flow
self.timeFwd()
self.resetRNG()
# Initialize the shock histories
updateBeliefHist = np.zeros((self.sim_periods, self.Nagents)) + np.nan
updateBeliefHist[0, :] = 1.0
t_idx = 0
# Loop through each simulated period
for t in range(1, self.sim_periods):
updateBeliefHist[t, :] = drawBernoulli(self.Nagents,
self.updateBeliefProb,
seed=self.RNG.randint(
0, 2**31 - 1))
# Advance the time index, looping if we've run out of income distributions
t_idx += 1
if t_idx >= len(self.IncomeDstn):
t_idx = 0
# Store the results as attributes of self and restore time to its original flow
self.updateBeliefHist = updateBeliefHist
if not orig_time:
self.timeRev()
def simMortality(self):
'''
Simulates the mortality process, killing off some percentage of agents
and replacing them with newborn agents.
Parameters
----------
none
Returns
-------
none
'''
if hasattr(self,'DiePrb'):
if self.DiePrb > 0:
who_dies = drawBernoulli(N=self.Nagents,p=self.DiePrb,seed=self.RNG.randint(low=1, high=2**31-1))
wealth_all = self.aNow*self.pNow
who_lives = np.logical_not(who_dies)
wealth_of_dead = np.sum(wealth_all[who_dies])
wealth_of_live = np.sum(wealth_all[who_lives])
R_actuarial = 1.0 + wealth_of_dead/wealth_of_live
self.aNow[who_dies] = 0.0
self.pNow[who_dies] = 1.0
self.aNow = self.aNow*R_actuarial