"aXtraCount":48, # Number of points in assets grid
"aXtraExtra":[None], # Additional points to include in assets grid
"aXtraNestFac":3, # Degree of exponential nesting when constructing assets grid
"LivPrb":[np.array([1.0,1.0])], # Survival probability
"DiscFac":base_primitives['DiscFac'], # Intertemporal discount factor
'Nagents':1, # Number of agents in a simulation (irrelevant)
'tax_rate':0.0, # Tax rate on labor income (irrelevant)
'vFuncBool':False, # Whether to calculate the value function
'CubicBool':True, # Whether to use cubic splines (False --> linear splines)
'MrkvArray':MrkvArray # State transition probabilities
}
MarkovType = MarkovConsumerType(**init_consumer_objects) # Make a basic consumer type
employed_income_dist = [np.ones(1),np.ones(1),np.ones(1)] # Income distribution when employed
unemployed_income_dist = [np.ones(1),np.ones(1),np.zeros(1)] # Income distribution when permanently unemployed
MarkovType.IncomeDstn = [[employed_income_dist,unemployed_income_dist]] # set the income distribution in each state
MarkovType.MrkvArray = MrkvArray
MarkovType.cycles = 0
# Solve the "Markov TBS" model
t_start = clock()
MarkovType.solve()
t_end = clock()
MarkovType.unpackcFunc()
print('Solving the same model "the long way" took ' + str(t_end-t_start) + ' seconds.')
#plotFuncs([ExampleType.solution[0].cFunc,ExampleType.solution[0].cFunc_U],0,m_upper)
plotFuncs(MarkovType.cFunc[0],0,m_upper)
diffFunc = lambda m : ExampleType.solution[0].cFunc(m) - MarkovType.cFunc[0][0](m)
print('Difference between the (employed) consumption functions:')
plotFuncs(diffFunc,0,m_upper)
False, # Whether to calculate the value function
'CubicBool':
True, # Whether to use cubic splines (False --> linear splines)
'MrkvArray':
MrkvArray # State transition probabilities
}
MarkovType = MarkovConsumerType(
**init_consumer_objects) # Make a basic consumer type
employed_income_dist = [np.ones(1), np.ones(1),
np.ones(1)] # Income distribution when employed
unemployed_income_dist = [
np.ones(1), np.ones(1), np.zeros(1)
] # Income distribution when permanently unemployed
MarkovType.IncomeDstn = [[employed_income_dist, unemployed_income_dist]
] # set the income distribution in each state
MarkovType.MrkvArray = MrkvArray
MarkovType.cycles = 0
# Solve the "Markov TBS" model
t_start = clock()
MarkovType.solve()
t_end = clock()
MarkovType.unpackcFunc()
print('Solving the same model "the long way" took ' +
str(t_end - t_start) + ' seconds.')
#plotFuncs([ExampleType.solution[0].cFunc,ExampleType.solution[0].cFunc_U],0,m_upper)
plotFuncs(MarkovType.cFunc[0], 0, m_upper)
diffFunc = lambda m: ExampleType.solution[0].cFunc(m) - MarkovType.cFunc[
0][0](m)
print('Difference between the (employed) consumption functions:')
