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()
cTranImpulseResponse = AggCons_impulseResponseInd(IndShockExample, PermShk,
TranShk, 100, 25)
plt.plot(cTranImpulseResponse)
print(
'Impulse response to a one time permanent and transitive shock to income of 10%:'
)
plt.show()
##########################################
# Now do aggregate shocks of a market
# Make an aggregate shocks consumer
AggShockExample = AggShockConsumerType(**Params.init_agg_shocks)
AggShockExample.cycles = 0
AggShockExample.sim_periods = 3000
AggShockExample.makeIncShkHist(
) # Simulate a history of idiosyncratic shocks
# Make a Cobb-Douglas economy for the agents
EconomyExample = CobbDouglasEconomy(agents=[AggShockExample],
act_T=AggShockExample.sim_periods,
**Params.init_cobb_douglas)
EconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks
# Have the consumers inherit relevant objects from the economy
AggShockExample.getEconomyData(EconomyExample)
# Solve the microeconomic model for the aggregate shocks example type (and display results)
t_start = clock()
AggShockExample.solve()
t_end = clock()
print('Solving an aggregate shocks consumer took ' +
mystr(t_end - t_start) + ' seconds.')
import ConsumerParameters as Params
from time import clock
from HARKutilities import plotFuncs
mystr = lambda number: "{:.4f}".format(number)
# Make an aggregate shocks sticky expectations consumer
StickyExample = AggShockStickyExpectationsConsumerType(
**Params.init_sticky_shocks)
NotStickyExample = AggShockConsumerType(**Params.init_sticky_shocks)
StickyExample.cycles = 0
NotStickyExample.cycles = 0
StickyExample.sim_periods = 3000
NotStickyExample.sim_periods = 3000
StickyExample.makeIncShkHist(
) # Simulate a history of idiosyncratic shocks
NotStickyExample.makeIncShkHist(
) # Simulate a history of idiosyncratic shocks
# Make a Cobb-Douglas economy for the agents
StickyEconomyExample = CobbDouglasEconomy(agents=[StickyExample],
act_T=StickyExample.sim_periods,
**Params.init_cobb_douglas)
NotStickyEconomyExample = CobbDouglasEconomy(
agents=[NotStickyExample],
act_T=NotStickyExample.sim_periods,
**Params.init_cobb_douglas)
StickyEconomyExample.makeAggShkHist(
) # Simulate a history of aggregate shocks
NotStickyEconomyExample.makeAggShkHist(
) # Simulate a history of aggregate shocks
# Have the consumers inherit relevant objects from the economy
