def makeCSTWresults(DiscFac, nabla, save_name=None):
'''
Produces a variety of results for the cstwMPC paper (usually after estimating).
Parameters
----------
DiscFac : float
Center of the uniform distribution of discount factors
nabla : float
Width of the uniform distribution of discount factors
save_name : string
Name to save the calculated results, for later use in producing figures
and tables, etc.
Returns
-------
none
'''
DiscFac_list = approxUniform(N=Params.pref_type_count,
bot=DiscFac - nabla,
top=DiscFac + nabla)[1]
assignBetaDistribution(est_type_list, DiscFac_list)
multiThreadCommandsFake(est_type_list, beta_point_commands)
lorenz_distance = np.sqrt(betaDistObjective(nabla))
makeCSTWstats(DiscFac, nabla, est_type_list, Params.age_weight_all,
lorenz_distance, save_name)
def calcKappaMean(DiscFac, nabla):
'''
Calculates the average MPC for the given parameters. This is a very small
sub-function of sensitivityAnalysis.
Parameters
----------
DiscFac : float
Center of the uniform distribution of discount factors
nabla : float
Width of the uniform distribution of discount factors
Returns
-------
kappa_all : float
Average marginal propensity to consume in the population.
'''
DiscFac_list = approxUniform(N=Params.pref_type_count,
bot=DiscFac - nabla,
top=DiscFac + nabla)[1]
assignBetaDistribution(est_type_list, DiscFac_list)
multiThreadCommandsFake(est_type_list, beta_point_commands)
kappa_all = calcWeightedAvg(
np.vstack((this_type.kappa_history for this_type in est_type_list)),
np.tile(Params.age_weight_all / float(Params.pref_type_count),
Params.pref_type_count))
return kappa_all
def simulateKYratioDifference(DiscFac, nabla, N, type_list, weights,
total_output, target):
'''
Assigns a uniform distribution over DiscFac with width 2*nabla and N points, then
solves and simulates all agent types in type_list and compares the simuated
K/Y ratio to the target K/Y ratio.
Parameters
----------
DiscFac : float
Center of the uniform distribution of discount factors.
nabla : float
Width of the uniform distribution of discount factors.
N : int
Number of discrete consumer types.
type_list : [cstwMPCagent]
List of agent types to solve and simulate after assigning discount factors.
weights : np.array
Age-conditional array of population weights.
total_output : float
Total output of the economy, denominator for the K/Y calculation.
target : float
Target level of capital-to-output ratio.
Returns
-------
my_diff : float
Difference between simulated and target capital-to-output ratios.
'''
if type(DiscFac) in (list, np.ndarray, np.array):
DiscFac = DiscFac[0]
DiscFac_list = approxUniform(N, DiscFac - nabla, DiscFac +
nabla)[1] # only take values, not probs
assignBetaDistribution(type_list, DiscFac_list)
multiThreadCommandsFake(type_list, beta_point_commands)
my_diff = calculateKYratioDifference(
np.vstack((this_type.W_history for this_type in type_list)),
np.tile(weights / float(N), N), total_output, target)
return my_diff
def test_multiThreadCommandsFake(self):
# check None return if it passes
self.assertIsNone(multiThreadCommandsFake(self.agents, ["solve()"]))
# check if an undefined method of agent is called
self.assertRaises(AttributeError, multiThreadCommandsFake, self.agents,
["foobar"])
BasicType.vFuncBool = False # just in case it was set to True above
my_agent_list = []
CRRA_list = np.linspace(
1, 8, type_count) # All the values that CRRA will take on
for i in range(type_count):
this_agent = deepcopy(BasicType) # Make a new copy of the basic type
this_agent.assignParameters(
CRRA=CRRA_list[i]) # Give it a unique CRRA value
my_agent_list.append(this_agent) # Addd it to the list of agent types
# Make a list of commands to be run in parallel; these should be methods of ConsumerType
do_this_stuff = ['updateSolutionTerminal()', 'solve()', 'unpackcFunc()']
# Solve the model for each type by looping over the types (not multithreading)
start_time = clock()
multiThreadCommandsFake(my_agent_list,
do_this_stuff) # Fake multithreading, just loops
end_time = clock()
print('Solving ' + str(type_count) +
' types without multithreading took ' +
mystr(end_time - start_time) + ' seconds.')
# Plot the consumption functions for all types on one figure
plotFuncs([this_type.cFunc[0] for this_type in my_agent_list], 0, 5)
# Delete the solution for each type to make sure we're not just faking it
for i in range(type_count):
my_agent_list[i].solution = None
my_agent_list[i].cFunc = None
my_agent_list[i].time_vary.remove('solution')
my_agent_list[i].time_vary.remove('cFunc')