def getRisky(self):
'''
Sets the attribute RiskyNow as a single draw from a lognormal distribution.
Uses the attributes RiskyAvgTrue and RiskyStdTrue if RiskyAvg is time-varying,
else just uses the single values from RiskyAvg and RiskyStd.
Parameters
----------
None
Returns
-------
None
'''
if 'RiskyDstn' in self.time_vary:
RiskyAvg = self.RiskyAvgTrue
RiskyStd = self.RiskyStdTrue
else:
RiskyAvg = self.RiskyAvg
RiskyStd = self.RiskyStd
RiskyAvgSqrd = RiskyAvg**2
RiskyVar = RiskyStd**2
mu = np.log(RiskyAvg / (np.sqrt(1. + RiskyVar / RiskyAvgSqrd)))
sigma = np.sqrt(np.log(1. + RiskyVar / RiskyAvgSqrd))
self.RiskyNow = drawLognormal(1,
mu=mu,
sigma=sigma,
seed=self.RNG.randint(0, 2**31 - 1))
def simBirth(self, which_agents):
'''
Makes new consumers for the given indices. Initialized variables include aNrm, as
well as time variables t_age and t_cycle. Normalized assets are drawn from a lognormal
distributions given by aLvlInitMean and aLvlInitStd.
Parameters
----------
which_agents : np.array(Bool)
Boolean array of size self.AgentCount indicating which agents should be "born".
Returns
-------
None
'''
# Get and store states for newly born agents
N = np.sum(which_agents) # Number of new consumers to make
self.aLvlNow[which_agents] = drawLognormal(N,
mu=self.aLvlInitMean,
sigma=self.aLvlInitStd,
seed=self.RNG.randint(
0, 2**31 - 1))
self.eStateNow[which_agents] = 1.0 # Agents are born employed
self.t_age[
which_agents] = 0 # How many periods since each agent was born
self.t_cycle[
which_agents] = 0 # Which period of the cycle each agent is currently in
return None
def test_drawLognormal(self):
self.assertEqual(simulation.drawLognormal(1)[0], 5.836039190663969)
# 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 goth_r = mu + sigma_r**2 / 2 beta = agent.DiscFac