def calculateLorenzDifference(sim_wealth, weights, percentiles, target_levels):
'''
Calculates the sum of squared differences between the simulatedLorenz curve
at the specified percentile levels and the target Lorenz levels.
Parameters:
-------------
sim_wealth : numpy.array
Array with simulated wealth values.
weights : numpy.array
List of weights for each row of sim_wealth.
percentiles : [float]
Points in the distribution of wealth to match.
target_levels : np.array
Actual U.S. Lorenz curve levels at the specified percentiles.
Returns:
-----------
distance : float
Sum of squared distances between simulated and target Lorenz curves.
'''
sim_lorenz = getLorenzPercentiles(sim_wealth,
weights=weights,
percentiles=percentiles)
distance = sum((100 * sim_lorenz - 100 * target_levels)**2)
return distance
def makeLorenzFig(real_wealth,real_weights,sim_wealth,sim_weights):
'''
Produces a Lorenz curve for the distribution of wealth, comparing simulated
to actual data. A sub-function of makeCSTWresults().
'''
these_percents = np.linspace(0.0001,0.9999,201)
real_lorenz = getLorenzPercentiles(real_wealth,weights=real_weights,percentiles=these_percents)
sim_lorenz = getLorenzPercentiles(sim_wealth,weights=sim_weights,percentiles=these_percents)
plt.plot(100*these_percents,real_lorenz,'-k',linewidth=1.5)
plt.plot(100*these_percents,sim_lorenz,'--k',linewidth=1.5)
plt.xlabel('Wealth percentile',fontsize=14)
plt.ylabel('Cumulative wealth ownership',fontsize=14)
plt.title('Simulated vs Actual Lorenz Curves',fontsize=16)
plt.legend(('Actual','Simulated'),loc=2,fontsize=12)
plt.ylim(-0.01,1)
plt.show()
return (these_percents,real_lorenz,sim_lorenz)
def makeLorenzFig(real_wealth, real_weights, sim_wealth, sim_weights):
'''
Produces a Lorenz curve for the distribution of wealth, comparing simulated
to actual data. A sub-function of makeCSTWresults().
'''
these_percents = np.linspace(0.0001, 0.9999, 201)
real_lorenz = getLorenzPercentiles(real_wealth,
weights=real_weights,
percentiles=these_percents)
sim_lorenz = getLorenzPercentiles(sim_wealth,
weights=sim_weights,
percentiles=these_percents)
plt.plot(100 * these_percents, real_lorenz, '-k', linewidth=1.5)
plt.plot(100 * these_percents, sim_lorenz, '--k', linewidth=1.5)
plt.xlabel('Wealth percentile', fontsize=14)
plt.ylabel('Cumulative wealth ownership', fontsize=14)
plt.title('Simulated vs Actual Lorenz Curves', fontsize=16)
plt.legend(('Actual', 'Simulated'), loc=2, fontsize=12)
plt.ylim(-0.01, 1)
plt.show()
return (these_percents, real_lorenz, sim_lorenz)
def calculateLorenzDifference(sim_wealth,weights,percentiles,target_levels):
'''
Calculates the sum of squared differences between the simulatedLorenz curve
at the specified percentile levels and the target Lorenz levels.
Parameters:
-------------
sim_wealth : numpy.array
Array with simulated wealth values.
weights : numpy.array
List of weights for each row of sim_wealth.
percentiles : [float]
Points in the distribution of wealth to match.
target_levels : np.array
Actual U.S. Lorenz curve levels at the specified percentiles.
Returns:
-----------
distance : float
Sum of squared distances between simulated and target Lorenz curves.
'''
sim_lorenz = getLorenzPercentiles(sim_wealth,weights=weights,percentiles=percentiles)
distance = sum((100*sim_lorenz-100*target_levels)**2)
return distance
np.tile(Params.age_weight_short / float(Params.pref_type_count),
Params.pref_type_count))
return kappa_all
# =================================================================
# ====== Make the list of consumer types for estimation ===========
#==================================================================
# Set target Lorenz points and K/Y ratio (MOVE THIS TO SetupParams)
if Params.do_liquid:
lorenz_target = np.array([0.0, 0.004, 0.025, 0.117])
KY_target = 6.60
else: # This is hacky until I can find the liquid wealth data and import it
lorenz_target = getLorenzPercentiles(
Params.SCF_wealth,
weights=Params.SCF_weights,
percentiles=Params.percentiles_to_match)
KY_target = 10.26
# Make a vector of initial wealth-to-permanent income ratios
w0_vector = simulateDiscreteDistribution(P=Params.w0_probs,
X=Params.w0_values,
N=Params.sim_pop_size,
seed=Params.w0_seed)
# Make the list of types for this run, whether infinite or lifecycle
if Params.do_lifecycle:
# Make base consumer types for each education level
DropoutType = cstwMPCagent(**Params.init_dropout)
DropoutType.w0 = w0_vector
HighschoolType = deepcopy(DropoutType)
return kappa_all
# Only run below this line if module is run rather than imported:
if __name__ == "__main__":
# =================================================================
# ====== Make the list of consumer types for estimation ===========
#==================================================================
# Set target Lorenz points and K/Y ratio (MOVE THIS TO SetupParams)
if Params.do_liquid:
lorenz_target = np.array([0.0, 0.004, 0.025,0.117])
KY_target = 6.60
else: # This is hacky until I can find the liquid wealth data and import it
lorenz_target = getLorenzPercentiles(Params.SCF_wealth,weights=Params.SCF_weights,percentiles=Params.percentiles_to_match)
#lorenz_target = np.array([-0.002, 0.01, 0.053,0.171])
KY_target = 10.26
# Make a vector of initial wealth-to-permanent income ratios
w0_vector = generateDiscreteDraws(P=Params.w0_probs,
X=Params.w0_values,
N=Params.sim_pop_size,
seed=Params.w0_seed)
# Make the list of types for this run, whether infinite or lifecycle
if Params.do_lifecycle:
# Make base consumer types for each education level
DropoutType = cstwMPCagent(**Params.init_dropout)
DropoutType.w0 = w0_vector