def getShocks(self):
'''
Finds the effective permanent and transitory shocks this period by combining the aggregate
and idiosyncratic shocks of each type.
Parameters
----------
None
Returns
-------
None
'''
IndShockConsumerType.getShocks(self) # Update idiosyncratic shocks
self.TranShkNow = self.TranShkNow * self.TranShkAggNow * self.wRteNow
self.PermShkNow = self.PermShkNow * self.PermShkAggNow
def getShocks(self):
'''
Finds the effective permanent and transitory shocks this period by combining the aggregate
and idiosyncratic shocks of each type.
Parameters
----------
None
Returns
-------
None
'''
IndShockConsumerType.getShocks(self) # Update idiosyncratic shocks
self.TranShkNow = self.TranShkNow*self.TranShkAggNow*self.wRteNow
self.PermShkNow = self.PermShkNow*self.PermShkAggNow
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 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