def perturbParameterToGetcPlotList(base_dictionary,
param_name,
param_min,
param_max,
N=20,
time_vary=False):
param_vec = np.linspace(
param_min, param_max, num=N, endpoint=True
) # vector of alternative values of the parameter to examine
thisConsumer = IndShockConsumerType(
**my_dictionary) # create an instance of the consumer type
thisConsumer.cycles = 0 # Make this type have an infinite horizon
x = np.linspace(
mMinVal, mMaxVal, xPoints, endpoint=True
) # Define a vector of x values that span the range from the minimum to the maximum values of m
for j in range(
N): # loop from the first to the last values of the parameter
if time_vary: # Some parameters are time-varying; others are not
setattr(thisConsumer, param_name, [param_vec[j]])
else:
setattr(thisConsumer, param_name, param_vec[j])
thisConsumer.update(
) # set up the preliminaries required to solve the problem
thisConsumer.solve() # solve the problem
y = thisConsumer.solution[0].cFunc(
x
) # Get the values of the consumption function at the points in the vector of x points
pylab.plot(
x, y, label=str(round(param_vec[j], 3))
) # plot it and generate a label indicating the rounded value of the parameter
pylab.legend(loc='upper right') # put the legend in the upper right
return pylab # return the figure
## There is one more parameter value we need to change. This one is more complicated than the rest. ## We could solve the problem for a consumer with an infinite horizon of periods that (ex-ante) ## are all identical. We could also solve the problem for a consumer with a fininite lifecycle, ## or for a consumer who faces an infinite horizon of periods that cycle (e.g., a ski instructor ## facing an infinite series of winters, with lots of income, and summers, with very little income.) ## The way to differentiate is through the "cycles" attribute, which indicates how often the ## sequence of periods needs to be solved. The default value is 1, for a consumer with a finite ## lifecycle that is only experienced 1 time. A consumer who lived that life twice in a row, and ## then died, would have cycles = 2. But neither is what we want. Here, we need to set cycles = 0, ## to tell HARK that we are solving the model for an infinite horizon consumer. ## Note that another complication with the cycles attribute is that it does not come from ## Params.init_idiosyncratic_shocks. Instead it is a keyword argument to the __init__() method of ## IndShockConsumerType. BaselineExample.cycles = 0 #################################################################################################### #################################################################################################### """ Now, create another consumer to compare the BaselineExample to. """ # The easiest way to begin creating the comparison example is to just copy the baseline example. # We can change the parameters we want to change later. from copy import deepcopy XtraCreditExample = deepcopy(BaselineExample)
# Remove TimeBefore periods
cAggImpulseResponse = cAggImpulseResponse[TimeBefore:]
return cAggImpulseResponse
###############################################################################
if __name__ == '__main__':
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)
## There is one more parameter value we need to change. This one is more complicated than the rest. ## We could solve the problem for a consumer with an infinite horizon of periods that (ex-ante) ## are all identical. We could also solve the problem for a consumer with a fininite lifecycle, ## or for a consumer who faces an infinite horizon of periods that cycle (e.g., a ski instructor ## facing an infinite series of winters, with lots of income, and summers, with very little income.) ## The way to differentiate is through the "cycles" attribute, which indicates how often the ## sequence of periods needs to be solved. The default value is 1, for a consumer with a finite ## lifecycle that is only experienced 1 time. A consumer who lived that life twice in a row, and ## then died, would have cycles = 2. But neither is what we want. Here, we need to set cycles = 0, ## to tell HARK that we are solving the model for an infinite horizon consumer. ## Note that another complication with the cycles attribute is that it does not come from ## Params.init_idiosyncratic_shocks. Instead it is a keyword argument to the __init__() method of ## IndShockConsumerType. BaselineExample.cycles = 0 #################################################################################################### #################################################################################################### """ Now, create another consumer to compare the BaselineExample to. """ # The easiest way to begin creating the comparison example is to just copy the baseline example. # We can change the parameters we want to change later. from copy import deepcopy XtraCreditExample = deepcopy(BaselineExample) # Now, change whatever parameters we want.
print('Solving a perfect foresight consumer took ' +
mystr(end_time - start_time) + ' seconds.')
PFexample.unpackcFunc()
PFexample.timeFwd()
# Plot the perfect foresight consumption function
print('Linear consumption function:')
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)
