params.update({
"BoroCnstArt": 0.0,
"vFuncBool": False,
"CubicBool": False,
"aXtraMin": 0.001,
"aXtraMax": 50.0,
"aXtraNestFac": 3,
"aXtraCount": 48,
"aXtraExtra": [None],
})
example = ConsIndShockModel.IndShockConsumerType(**params)
else:
params = copy.deepcopy(params)
params.pop("PermShkStd", None)
params.pop("TranShkStd", None)
example = ConsIndShockModel.PerfForesightConsumerType(**params)
examples.append(example)
results.append(st.empty())
for i, example in enumerate(examples):
example.solve()
results[i].markdown(
f"**Model {1+i}** agent's human wealth is {example.solution[0].hNrm:.02f} times "
f"his current income level, with its consumption function is defined (consumption is "
f"positive) down to m_t = {example.solution[0].mNrmMin:.02f}.")
st.pyplot(
plotFuncs(
[e.solution[0].cFunc for e in examples],
0.0,
10,
# Make agent live for sure until the terminal period pf_dict['T_retire'] = t_cycle pf_dict['LivPrb'] = [1] * t_cycle # Shut down income growth pf_dict['PermGroFac'] = [1] * t_cycle # Decrease grid for speed pf_dict['aXtraCount'] = 100 # %% Create both agents port_agent = cpm.PortfolioConsumerType(**pf_dict) port_agent.solve() pf_agent = cis.PerfForesightConsumerType(**pf_dict) pf_agent.solve() # %% Construct the analytical solution rho = pf_dict['CRRA'] R = pf_dict['Rfree'] Beta = pf_dict['DiscFac'] T = pf_dict['T_cycle'] + 1 thorn_r = (R * Beta)**(1 / rho) / R ht = lambda t: (1 - (1 / R)**(T - t + 1)) / (1 - 1 / R) - 1 kappa = lambda t: (1 - thorn_r) / (1 - thorn_r**(T - t + 1)) true_cFunc = lambda t, m: np.minimum(m, kappa(t) * (m + ht(t)))