def setUp(self):
# Make one agent type and an economy for it to live in
self.agent = AggShockMarkovConsumerType()
self.agent.cycles = 0
self.agent.AgentCount = 1000 # Low number of simulated agents
self.agent.IncomeDstn[0] = 2 * [self.agent.IncomeDstn[0]] ## see #557
self.economy = CobbDouglasMarkovEconomy(agents=[self.agent])
class testAggShockMarkovConsumerType(unittest.TestCase):
def setUp(self):
# Make one agent type and an economy for it to live in
self.agent = AggShockMarkovConsumerType()
self.agent.cycles = 0
self.agent.AgentCount = 1000 # Low number of simulated agents
self.agent.IncomeDstn[0] = 2 * [self.agent.IncomeDstn[0]] ## see #557
self.economy = CobbDouglasMarkovEconomy(agents=[self.agent])
def test_agent(self):
# Have one consumer type inherit relevant objects from the economy,
# then solve their microeconomic problem
self.agent.getEconomyData(self.economy)
self.agent.solve()
self.assertAlmostEqual(
self.agent.solution[0].cFunc[0](10.0, self.economy.MSS),
2.5635896520991377)
def test_economy(self):
# Adjust the economy so that it (fake) solves quickly
self.economy.act_T = 500 # Short simulation history
self.economy.max_loops = 3 # Just quiet solving early
self.economy.verbose = False # Turn off printed messages
self.agent.getEconomyData(self.economy)
self.economy.makeAggShkHist(
) # Make a simulated history of aggregate shocks
self.economy.solve(
) # Solve for the general equilibrium of the economy
self.economy.AFunc = self.economy.dynamics.AFunc
self.assertAlmostEqual(self.economy.AFunc[0].slope, 1.0904698841958917)
self.assertAlmostEqual(self.economy.history["AaggNow"][5],
9.467758924955897)
def __init__(self, agents=[], tolerance=0.0001, act_T=1000, **kwds):
'''
Make a new instance of StickyCobbDouglasMarkovEconomy by filling in attributes
specific to this kind of market.
Parameters
----------
agents : [ConsumerType]
List of types of consumers that live in this economy.
tolerance: float
Minimum acceptable distance between "dynamic rules" to consider the
solution process converged. Distance depends on intercept and slope
of the log-linear "next capital ratio" function.
act_T : int
Number of periods to simulate when making a history of of the market.
Returns
-------
None
'''
CobbDouglasMarkovEconomy.__init__(self,
agents=agents,
tolerance=tolerance,
act_T=act_T,
**kwds)
self.reap_vars = ['aLvlNow', 'pLvlTrue']
class testAggShockMarkovConsumerType(unittest.TestCase):
def setUp(self):
self.agent = AggShockMarkovConsumerType()
self.agent.IncomeDstn[0] = 2*[self.agent.IncomeDstn[0]] ## see #557
self.economy = CobbDouglasMarkovEconomy(
agents = [self.agent])
def test_economy(self):
self.agent.getEconomyData(self.economy) # Makes attributes of the economy, attributes of the agent
self.economy.makeAggShkHist() # Make a simulated history of the economy
# Set tolerance level.
self.economy.tolerance = 0.5
'CRRA': 1.0,
'PermGroFacAgg': [1.0,1.0],
'AggregateL':1.0, # Fix aggregate labor supply at 1.0 - makes interpretation of z easier
'act_T':1200, # Number of periods for economy to run in simulation
'intercept_prev': [0.0,0.0], # Make some initial guesses at linear savings rule intercepts for each state
'slope_prev': [1.0,1.0], # Make some initial guesses at linear savings rule slopes for each state
'MrkvArray': np.array([[0.875,0.125],
[0.125,0.875]]), # Transition probabilities
'MrkvNow_init': 0 # Pick a state to start in (we pick the first state)
}
# The 'interesting' parts of the CobbDouglasMarkovEconomy
KSEconomyDictionary['CapShare'] = 0.36
KSEconomyDictionary['MrkvArray'] = AggMrkvArray
KSEconomy = CobbDouglasMarkovEconomy(agents = [KSAgent], **KSEconomyDictionary) # Combine production and consumption sides into an "Economy"
# %% [markdown]
# We have now populated the $\texttt{KSEconomy}$ with $\texttt{KSAgents}$ defined before. That is basically telling the agents to take the macro state from the $\texttt{KSEconomy}$.
#
# Now we construct the $\texttt{AggShkDstn}$ that specifies the evolution of the dynamics of the $\texttt{KSEconomy}$.
#
# The structure of the inputs for $\texttt{AggShkDstn}$ follows the same logic as for $\texttt{IncomeDstn}$. Now there is only one possible outcome for each aggregate state (the KS aggregate states are very simple), therefore, each aggregate state has only one possible condition which happens with probability 1.
# %% {"code_folding": []}
# Calibrate the magnitude of the aggregate shocks
Tran_g = 1.01 # Productivity z in the good aggregate state
Tran_b = 0.99 # and the bad state
# The HARK framework allows permanent shocks
def setUp(self):
self.agent = AggShockMarkovConsumerType()
self.agent.IncomeDstn[0] = 2 * [self.agent.IncomeDstn[0]] ## see #557
self.economy = CobbDouglasMarkovEconomy(agents=[self.agent])
c_at_this_M = AggShockExample.cFunc[0](m_grid + mMin,
M * np.ones_like(m_grid))
plt.plot(m_grid + mMin, c_at_this_M)
plt.ylim(0.0, None)
plt.show()
# ### Example Implementations of AggShockMarkovConsumerType
if solve_markov_micro or solve_markov_market or solve_krusell_smith:
# Make a Markov aggregate shocks consumer type
AggShockMrkvExample = AggShockMarkovConsumerType()
AggShockMrkvExample.IncomeDstn[0] = 2 * [AggShockMrkvExample.IncomeDstn[0]]
AggShockMrkvExample.cycles = 0
# Make a Cobb-Douglas economy for the agents
MrkvEconomyExample = CobbDouglasMarkovEconomy(agents=[AggShockMrkvExample])
MrkvEconomyExample.DampingFac = 0.2 # Turn down damping
MrkvEconomyExample.makeAggShkHist(
) # Simulate a history of aggregate shocks
AggShockMrkvExample.getEconomyData(
MrkvEconomyExample
) # Have the consumers inherit relevant objects from the economy
if solve_markov_micro:
# Solve the microeconomic model for the Markov aggregate shocks example type (and display results)
t_start = process_time()
AggShockMrkvExample.solve()
t_end = process_time()
print("Solving an aggregate shocks Markov consumer took " +
mystr(t_end - t_start) + " seconds.")
def consExample(filename):
KSAgentDictionary = {
"CRRA" : 3.0, # Coefficient of relative risk aversion
"DiscFac" : 0.99, # Intertemporal discount factor
"LivPrb" : [1.0], # Survival probability
"AgentCount" : 1000, # Number of agents of this type (only matters for simulation)
"aNrmInitMean" : 0.0, # Mean of log initial assets (only matters for simulation)
"aNrmInitStd" : 0.0, # Standard deviation of log initial assets (only for simulation)
"pLvlInitMean" : 0.0, # Mean of log initial permanent income (only matters for simulation)
"pLvlInitStd" : 0.0, # Standard deviation of log initial permanent income (only matters for simulation)
"PermGroFacAgg" : 1.0, # Aggregate permanent income growth factor (only matters for simulation)
"T_age" : None, # Age after which simulated agents are automatically killed
"T_cycle" : 1, # Number of periods in the cycle for this agent type
#
# Parameters for constructing the "assets above minimum" grid
"aXtraMin" : 0.001, # Minimum end-of-period "assets above minimum" value
"aXtraMax" : 20, # Maximum end-of-period "assets above minimum" value
"aXtraExtra" : [None], # Some other value of "assets above minimum" to add to the grid
"aXtraNestFac" : 3, # Exponential nesting factor when constructing "assets above minimum" grid
"aXtraCount" : 24, # Number of points in the grid of "assets above minimum"
#
# Parameters describing the income process
"PermShkCount" : 1, # Number of points in discrete approximation to permanent income shocks - no shocks of this kind!
"TranShkCount" : 1, # Number of points in discrete approximation to transitory income shocks - no shocks of this kind!
"PermShkStd" : [0.], # Standard deviation of log permanent income shocks - no shocks of this kind!
"TranShkStd" : [0.], # Standard deviation of log transitory income shocks - no shocks of this kind!
"UnempPrb" : 0.0, # Probability of unemployment while working - no shocks of this kind!
"UnempPrbRet" : 0.0, # Probability of "unemployment" while retired - no shocks of this kind!
"IncUnemp" : 0.0, # Unemployment benefits replacement rate
"IncUnempRet" : 0.0, # "Unemployment" benefits when retired
"tax_rate" : 0.0, # Flat income tax rate
"T_retire" : 0, # Period of retirement (0 --> no retirement)
"BoroCnstArt" : 0.0, # Artificial borrowing constraint; imposed minimum level of end-of period assets
"cycles" : 0, # Consumer is infinitely lived
"PermGroFac" : [1.0], # Permanent income growth factor
#
# New Parameters that we need now
'MgridBase': np.array([0.1, 0.3, 0.6,
0.8, 0.9, 0.98,
1.0, 1.02, 1.1,
1.2, 1.6, 2.0,
3.0]), # Grid of capital-to-labor-ratios (factors)
'MrkvArray': np.array([[0.875, 0.125],
[0.125, 0.875]]),# Transition probabilities for macroecon. [i,j] is probability of being in state j next
# period conditional on being in state i this period.
'PermShkAggStd' : [0.0, 0.0], # Standard deviation of log aggregate permanent shocks by state. No continous shocks in a state.
'TranShkAggStd' : [0.0, 0.0], # Standard deviation of log aggregate transitory shocks by state. No continuous shocks in a state.
'PermGroFacAgg' : 1.0
}
# Economy Dictionary
KSEconomyDictionary = {
'PermShkAggCount': 1,
'TranShkAggCount': 1,
'PermShkAggStd' : [0.0, 0.0],
'TranShkAggStd' : [0.0, 0.0],
'DeprFac' : 0.025, # Depreciation factor
'CapShare' : 0.36, # Share of capital income in cobb-douglas production function
'DiscFac' : 0.99,
'CRRA' : 3.0,
'PermGroFacAgg' : [1.0, 1.0],
'AggregateL' : 1.0, # Fix aggregate labor supply at 1.0 - makes interpretation of z easier
'act_T' : 1200, # Number of periods for economy to run in simulation
'intercept_prev' : [0.0, 0.0], # Make some initial guesses at linear savings rule intercepts for each state
'slope_prev' : [1.0, 1.0], # Make some initial guesses at linear savings rule slopes for each state
'MrkvArray' : np.array([ [0.875, 0.125],
[0.125, 0.875]]), # Transition probabilities
'MrkvNow_init' : 0 # Pick a state to start in (we pick the first state)
}
### Representative Agent
# Create the Krusell-Smith agent as an instance of AggShockMarkovConsumerType
KSAgent = AggShockMarkovConsumerType(**KSAgentDictionary)
# Construct the income distribution for the Krusell-Smith agent
prb_eg = 0.96 # Probability of employment in the good state
prb_ug = 1 - prb_eg # Probability of unemployment in the good state
prb_eb = 0.90 # Probability of employment in the bad state
prb_ub = 1 - prb_eb # Probability of unemployment in the bad state
p_ind = 1 # Persistent component of income is always 1
ell_ug = ell_ub = 0 # Labor supply is zero for unemployed consumers in either agg state
ell_eg = 1.0/prb_eg # Labor supply for employed consumer in good state
ell_eb = 1.0/prb_eb # 1=pe_g*ell_ge+pu_b*ell_gu=pe_b*ell_be+pu_b*ell_gu
# IncomeDstn is a list of lists, one for each aggregate Markov state
# Each contains three arrays of floats, representing a discrete approximation to the income process.
# Order:
# state probabilities
# idiosyncratic persistent income level by state (KS have no persistent shocks p_ind is always 1.0)
# idiosyncratic transitory income level by state
KSAgent.IncomeDstn[0] = [
[np.array([prb_eg,prb_ug]), np.array([p_ind,p_ind]), np.array([ell_eg,ell_ug])], # Agg state good
[np.array([prb_eb,prb_ub]), np.array([p_ind,p_ind]), np.array([ell_eb,ell_ub])] # Agg state bad
]
KSEconomy = CobbDouglasMarkovEconomy(agents = [KSAgent], **KSEconomyDictionary) # Combine production and consumption sides into an "Economy"
# Calibrate the magnitude of the aggregate shocks
Tran_g = 1.01 # Productivity z in the good aggregate state
Tran_b = 0.99 # and the bad state
# Aggregate productivity shock distribution by state.
# First element is probabilities of different outcomes, given the state you are in.
# Second element is agg permanent shocks (here we don't have any, so just they are just 1.).
# Third element is agg transitory shocks, which are calibrated the same as in Krusell Smith.
KSAggShkDstn = [
[np.array([1.0]),np.array([1.0]),np.array([Tran_g])], # Aggregate good
[np.array([1.0]),np.array([1.0]),np.array([Tran_b])] # Aggregate bad
]
KSEconomy.AggShkDstn = KSAggShkDstn
# Construct the economy, make an initial history, then solve
KSAgent.getEconomyData(KSEconomy) # Makes attributes of the economy, attributes of the agent
KSEconomy.makeAggShkHist() # Make a simulated history of the economy
# Tolerance level
KSEconomy.tolerance = 0.01
# Solve the economy using the market method.
KSEconomy.solve()
### Plots
print('Consumption function at each aggregate market resources gridpoint (in general equilibrium):')
m_grid = np.linspace(0,20,200)
c_m = KSAgent.solution[0].cFunc[0](m_grid,10.0*np.ones_like(m_grid))
f = plt.figure()
plt.plot(m_grid,c_m, color = '#4a69bd', linewidth = 2.5)
plt.ylabel(r'$c(y)$', labelpad = 15, rotation = 0)
plt.ylim([-0.15, 2.15])
plt.yticks([0 + 0.25 * k for k in range(9)], [r'${:.1f}$'.format(0 + 0.25 * k) if k % 2 == 0 else '' for k in range(9)])
plt.xlabel(r'$y$')
plt.xlim([-1, 21])
plt.xticks([0 + 2.5 * k for k in range(9)], [r'${:.0f}$'.format(0 + 2.5 * k) if k % 2 == 0 else '' for k in range(9)])
plt.show()
f.savefig(filename, bbox_inches='tight')
