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
def update(self):
'''
Updates the assets grid, income process, terminal period solution, and
preference shock process. A very slight extension of IndShockConsumerType.update()
for the preference shock model.
Parameters
----------
None
Returns
-------
None
'''
IndShockConsumerType.update(self) # Update assets grid, income process, terminal solution
self.updatePrefShockProcess() # Update the discrete preference shock process
def update(self):
'''
Updates the assets grid, income process, terminal period solution, and
preference shock process. A very slight extension of IndShockConsumerType.update()
for the preference shock model.
Parameters
----------
None
Returns
-------
None
'''
IndShockConsumerType.update(self) # Update assets grid, income process, terminal solution
self.updatePrefShockProcess() # Update the discrete preference shock process
