def distributeParams(self, param_name, param_count, center, spread,
dist_type):
'''
Distributes heterogeneous values of one parameter to the AgentTypes in self.agents.
Parameters
----------
param_name : string
Name of the parameter to be assigned.
param_count : int
Number of different values the parameter will take on.
center : float
A measure of centrality for the distribution of the parameter.
spread : float
A measure of spread or diffusion for the distribution of the parameter.
dist_type : string
The type of distribution to be used. Can be "lognormal" or "uniform" (can expand).
Returns
-------
None
'''
# Get a list of discrete values for the parameter
if dist_type == 'uniform':
# If uniform, center is middle of distribution, spread is distance to either edge
param_dist = approxUniform(N=param_count,
bot=center - spread,
top=center + spread)
elif dist_type == 'lognormal':
# If lognormal, center is the mean and spread is the standard deviation (in log)
tail_N = 3
param_dist = approxLognormal(N=param_count - tail_N,
mu=np.log(center) - 0.5 * spread**2,
sigma=spread,
tail_N=tail_N,
tail_bound=[0.0, 0.9],
tail_order=np.e)
# Distribute the parameters to the various types, assigning consecutive types the same
# value if there are more types than values
replication_factor = len(self.agents) // param_count
# Note: the double division is intenger division in Python 3 and 2.7, this makes it explicit
j = 0
b = 0
while j < len(self.agents):
for n in range(replication_factor):
self.agents[j](AgentCount=int(self.Population *
param_dist[0][b] *
self.TypeWeight[n]))
exec('self.agents[j](' + param_name + '= param_dist[1][b])')
j += 1
b += 1
# Make new dictionary mpc_dict = copy(dict_portfolio) # Make riskless return factor very low so that nobody invests in it. mpc_dict['Rfree'] = 0.01 # Make the agent less risk averse mpc_dict['CRRA'] = 2 # Do away with probability of death mpc_dict['LivPrb'] = [1] * dict_portfolio['T_cycle'] # Risky returns mu = 0.05 std = 0.1 # Construct the distribution approximation for integration RiskyDstnFunc = lambda count: approxLognormal(count, mu=mu, sigma=std) # Contruct function for drawing returns RiskyDrawFunc = lambda seed: drawLognormal(1, mu=mu, sigma=std, seed=seed) mpc_dict['approxRiskyDstn'] = RiskyDstnFunc mpc_dict['drawRiskyFunc'] = RiskyDrawFunc agent = cpm.PortfolioConsumerType(**mpc_dict) agent.cylces = 0 agent.solve() # %% Compute the theoretical MPC (for when there is no labor income) # First extract some parameter values that will be used crra = agent.CRRA sigma_r = std
