def setUp(self):
# Set up and solve infinite type
import ConsumerParameters as Params
InfiniteType = IndShockConsumerType(**Params.init_idiosyncratic_shocks)
InfiniteType.assignParameters(
LivPrb=[1.],
DiscFac=0.955,
PermGroFac=[1.],
PermShkStd=[0.],
TempShkStd=[0.],
T_total=1,
T_retire=0,
BoroCnstArt=None,
UnempPrb=0.,
cycles=0) # This is what makes the type infinite horizon
InfiniteType.updateIncomeProcess()
InfiniteType.solve()
InfiniteType.timeFwd()
InfiniteType.unpackcFunc()
# Make and solve a perfect foresight consumer type
PerfectForesightType = deepcopy(InfiniteType)
PerfectForesightType.solveOnePeriod = solvePerfForesight
PerfectForesightType.solve()
PerfectForesightType.unpackcFunc()
PerfectForesightType.timeFwd()
self.InfiniteType = InfiniteType
self.PerfectForesightType = PerfectForesightType
def setUp(self):
"""
Prepare to compare the models by initializing and solving them
"""
# Set up and solve infinite type
import ConsumerParameters as Params
InfiniteType = IndShockConsumerType(**Params.init_idiosyncratic_shocks)
InfiniteType.assignParameters(LivPrb = [1.],
DiscFac = 0.955,
PermGroFac = [1.],
PermShkStd = [0.],
TempShkStd = [0.],
T_total = 1, T_retire = 0, BoroCnstArt = None, UnempPrb = 0.,
cycles = 0) # This is what makes the type infinite horizon
InfiniteType.updateIncomeProcess()
InfiniteType.solve()
InfiniteType.timeFwd()
InfiniteType.unpackcFunc()
# Make and solve a perfect foresight consumer type with the same parameters
PerfectForesightType = deepcopy(InfiniteType)
PerfectForesightType.solveOnePeriod = solvePerfForesight
PerfectForesightType.solve()
PerfectForesightType.unpackcFunc()
PerfectForesightType.timeFwd()
self.InfiniteType = InfiniteType
self.PerfectForesightType = PerfectForesightType
import ConsumerParameters as Params
from time import clock
mystr = lambda number: "{:.4f}".format(number)
# Make and solve an example consumer with idiosyncratic income shocks
IndShockExample = IndShockConsumerType(**Params.init_idiosyncratic_shocks)
IndShockExample.cycles = 0 # Make this type have an infinite horizon
IndShockExample.DiePrb = 1 - IndShockExample.LivPrb[
0] # Not sure why this is not already in the object
start_time = clock()
IndShockExample.solve()
end_time = clock()
print('Solving a consumer with idiosyncratic shocks took ' +
mystr(end_time - start_time) + ' seconds.')
IndShockExample.unpackcFunc()
IndShockExample.timeFwd()
PermShk = 1.1
TranShk = 1
cPermImpulseResponse = AggCons_impulseResponseInd(IndShockExample, PermShk,
TranShk, 100, 25)
plt.plot(cPermImpulseResponse)
PermShk = 1
TranShk = 1.1
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%:'
mMin = PFexample.solution[0].mNrmMin
#plotFuncs(PFexample.cFunc[0],mMin,mMin+10)
###############################################################################
# Make and solve an example consumer with idiosyncratic income shocks
IndShockExample = IndShockConsumerType(**Params.init_idiosyncratic_shocks)
IndShockExample.cycles = 0 # Make this type have an infinite horizon
start_time = clock()
IndShockExample.solve()
end_time = clock()
print('Solving a consumer with idiosyncratic shocks took ' +
mystr(end_time - start_time) + ' seconds.')
IndShockExample.unpackcFunc()
IndShockExample.timeFwd()
# Plot the consumption function and MPC for the infinite horizon consumer
#print('Concave consumption function:')
#plotFuncs(IndShockExample.cFunc[0],IndShockExample.solution[0].mNrmMin,5)
#print('Marginal consumption function:')
#plotFuncsDer(IndShockExample.cFunc[0],IndShockExample.solution[0].mNrmMin,5)
# Compare the consumption functions for the perfect foresight and idiosyncratic
# shock types. Risky income cFunc asymptotically approaches perfect foresight cFunc.
print('Consumption functions for perfect foresight vs idiosyncratic shocks:')
#plotFuncs([PFexample.cFunc[0],IndShockExample.cFunc[0]],IndShockExample.solution[0].mNrmMin,10)
fig, ax1 = plt.subplots()
