def get_reduced_parameters():
# Model Parameters
S = 10
J = 2
T = int(2 * S)
lambdas = np.array([.50, .50])
starting_age = 40
ending_age = 50
E = int(starting_age * (S / float(ending_age-starting_age)))
beta_annual = .96
beta = beta_annual ** (float(ending_age-starting_age) / S)
sigma = 3.0
alpha = .35
Z = 1.0
delta_annual = .05
delta = 1 - ((1-delta_annual) ** (float(ending_age-starting_age) / S))
ltilde = 1.0
g_y_annual = 0.03
g_y = (1 + g_y_annual)**(float(ending_age-starting_age)/S) - 1
# Ellipse parameters
b_ellipse = 25.6594
k_ellipse = -26.4902
upsilon = 3.0542
# Tax parameters:
# Income Tax Parameters
mean_income_data = 84377.0
a_tax_income = 3.03452713268985e-06
b_tax_income = .222
c_tax_income = 133261.0
d_tax_income = .219
# Wealth tax params
# These are non-calibrated values, h and m just need
# need to be nonzero to avoid errors. When p_wealth
# is zero, there is no wealth tax.
h_wealth = 0.1
m_wealth = 1.0
p_wealth = 0.0
# Bequest and Payroll Taxes
tau_bq = np.zeros(J)
tau_payroll = 0.15
retire = np.round(9.0 * S / 16.0) - 1
# Simulation Parameters
MINIMIZER_TOL = 1e-3
MINIMIZER_OPTIONS = {'maxiter': 1}
PLOT_TPI = False
maxiter = 10
mindist_SS = 1e-3
mindist_TPI = 1e-6
nu = .4
flag_graphs = False
# Calibration parameters
# These guesses are close to the calibrated values
chi_b_guess = np.array([1, 100000])
chi_n_guess = np.array([5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
# Generate Income and Demographic parameters
omega, g_n_ss, omega_SS, surv_rate, rho, g_n_vector = get_omega(
S, T, starting_age, ending_age, E, flag_graphs)
e = np.array([[0.25, 1.25]] * 10)
allvars = dict(locals())
return allvars
def get_full_parameters():
# Model Parameters
S = 80
J = 7
T = int(2 * S)
lambdas = np.array([.25, .25, .2, .1, .1, .09, .01])
starting_age = 20
ending_age = 100
E = int(starting_age * (S / float(ending_age-starting_age)))
beta_annual = .96
beta = beta_annual ** (float(ending_age-starting_age) / S)
sigma = 3.0
alpha = .35
Z = 1.0
delta_annual = .05
delta = 1 - ((1-delta_annual) ** (float(ending_age-starting_age) / S))
ltilde = 1.0
g_y_annual = 0.03
g_y = (1 + g_y_annual)**(float(ending_age-starting_age)/S) - 1
# Ellipse parameters
b_ellipse = 25.6594
k_ellipse = -26.4902
upsilon = 3.0542
# Tax parameters:
# Income Tax Parameters
mean_income_data = 84377.0
a_tax_income = 3.03452713268985e-06
b_tax_income = .222
c_tax_income = 133261.0
d_tax_income = .219
# Wealth tax params
# These are non-calibrated values, h and m just need
# need to be nonzero to avoid errors. When p_wealth
# is zero, there is no wealth tax.
h_wealth = 0.1
m_wealth = 1.0
p_wealth = 0.0
# Bequest and Payroll Taxes
tau_bq = np.zeros(J)
tau_payroll = 0.15
retire = np.round(9.0 * S / 16.0) - 1
# Simulation Parameters
MINIMIZER_TOL = 1e-14
MINIMIZER_OPTIONS = None
PLOT_TPI = True
maxiter = 250
mindist_SS = 1e-9
mindist_TPI = 1e-6
nu = .4
flag_graphs = False
# Calibration parameters
# These guesses are close to the calibrated values
chi_b_guess = np.array([2, 10, 90, 350, 1700, 22000, 120000])
chi_n_guess = np.array([47.12000874 , 22.22762421 , 14.34842241 , 10.67954008 , 8.41097278
, 7.15059004 , 6.46771332 , 5.85495452 , 5.46242013 , 5.00364263
, 4.57322063 , 4.53371545 , 4.29828515 , 4.10144524 , 3.8617942 , 3.57282
, 3.47473172 , 3.31111347 , 3.04137299 , 2.92616951 , 2.58517969
, 2.48761429 , 2.21744847 , 1.9577682 , 1.66931057 , 1.6878927
, 1.63107201 , 1.63390543 , 1.5901486 , 1.58143606 , 1.58005578
, 1.59073213 , 1.60190899 , 1.60001831 , 1.67763741 , 1.70451784
, 1.85430468 , 1.97291208 , 1.97017228 , 2.25518398 , 2.43969757
, 3.21870602 , 4.18334822 , 4.97772026 , 6.37663164 , 8.65075992
, 9.46944758 , 10.51634777 , 12.13353793 , 11.89186997 , 12.07083882
, 13.2992811 , 14.07987878 , 14.19951571 , 14.97943562 , 16.05601334
, 16.42979341 , 16.91576867 , 17.62775142 , 18.4885405 , 19.10609921
, 20.03988031 , 20.86564363 , 21.73645892 , 22.6208256 , 23.37786072
, 24.38166073 , 25.22395387 , 26.21419653 , 27.05246704 , 27.86896121
, 28.90029708 , 29.83586775 , 30.87563699 , 31.91207845 , 33.07449767
, 34.27919965 , 35.57195873 , 36.95045988 , 38.62308152])
# Generate Income and Demographic parameters
omega, g_n_ss, omega_SS, surv_rate, rho, g_n_vector = get_omega(
S, T, starting_age, ending_age, E, flag_graphs)
e = get_e(S, J, starting_age, ending_age, lambdas, omega_SS, flag_graphs)
allvars = dict(locals())
return allvars
, 3.47473172 , 3.31111347 , 3.04137299 , 2.92616951 , 2.58517969
, 2.48761429 , 2.21744847 , 1.9577682 , 1.66931057 , 1.6878927
, 1.63107201 , 1.63390543 , 1.5901486 , 1.58143606 , 1.58005578
, 1.59073213 , 1.60190899 , 1.60001831 , 1.67763741 , 1.70451784
, 1.85430468 , 1.97291208 , 1.97017228 , 2.25518398 , 2.43969757
, 3.21870602 , 4.18334822 , 4.97772026 , 6.37663164 , 8.65075992
, 9.46944758 , 10.51634777 , 12.13353793 , 11.89186997 , 12.07083882
, 13.2992811 , 14.07987878 , 14.19951571 , 14.97943562 , 16.05601334
, 16.42979341 , 16.91576867 , 17.62775142 , 18.4885405 , 19.10609921
, 20.03988031 , 20.86564363 , 21.73645892 , 22.6208256 , 23.37786072
, 24.38166073 , 25.22395387 , 26.21419653 , 27.05246704 , 27.86896121
, 28.90029708 , 29.83586775 , 30.87563699 , 31.91207845 , 33.07449767
, 34.27919965 , 35.57195873 , 36.95045988 , 38.62308152])
# Demographic and Ability variables
omega, g_n_ss, omega_SS, surv_rate, rho, g_n_vector = demographics.get_omega(
S, T, starting_age, ending_age, E, flag_graphs)
e = income.get_e(S, J, starting_age, ending_age, lambdas, omega_SS, flag_graphs)
# List of parameter names that will not be changing (unless we decide to
# change them for a tax experiment)
param_names = ['S', 'J', 'T', 'lambdas', 'starting_age', 'ending_age',
'beta', 'sigma', 'alpha', 'nu', 'Z', 'delta', 'E',
'ltilde', 'g_y', 'maxiter', 'mindist_SS', 'mindist_TPI',
'b_ellipse', 'k_ellipse', 'upsilon',
'a_tax_income', 'chi_b_guess', 'chi_n_guess',
'b_tax_income', 'c_tax_income', 'd_tax_income',
'tau_payroll', 'tau_bq', 'calibrate_model',
'retire', 'mean_income_data', 'g_n_vector',
'h_wealth', 'p_wealth', 'm_wealth', 'get_baseline',
'omega', 'g_n_ss', 'omega_SS', 'surv_rate', 'e', 'rho']
------------------------------------------------------------------------
Generate income and demographic parameters
------------------------------------------------------------------------
e = S x J matrix of age dependent possible working abilities
e_s
omega = T x S x J array of demographics
g_n = steady state population growth rate
omega_SS = steady state population distribution
children = T x starting_age x J array of children demographics
surv_rate = S x 1 array of survival rates
mort_rate = S x 1 array of mortality rates
------------------------------------------------------------------------
'''
e = income.get_e(S, J, starting_age, ending_age, bin_weights)
omega, g_n, omega_SS, children, surv_rate = demographics.get_omega(
S, J, T, bin_weights, starting_age, ending_age, E)
mort_rate = 1 - surv_rate
surv_rate[-1] = 0.0
mort_rate[-1] = 1
'''
------------------------------------------------------------------------
Finding the Steady State
------------------------------------------------------------------------
K_guess_init = (S-1 x J) array for the initial guess of the distribution
of capital
L_guess_init = (S x J) array for the initial guess of the distribution
of labor
solutions = ((S * (S-1) * J * J) x 1) array of solutions of the
steady state distributions of capital and labor
Kssmat = ((S-1) x J) array of the steady state distribution of
------------------------------------------------------------------------
Generate income and demographic parameters
------------------------------------------------------------------------
e = S x J matrix of age dependent possible working abilities
e_s
omega = T x S x J array of demographics
g_n = steady state population growth rate
omega_SS = steady state population distribution
children = T x starting_age x J array of children demographics
surv_rate = S x 1 array of survival rates
mort_rate = S x 1 array of mortality rates
------------------------------------------------------------------------
'''
if SS_stage == 'first_run_for_guesses':
omega, g_n, omega_SS, children, surv_rate = demographics.get_omega(
S, J, T, bin_weights, starting_age, ending_age, E)
e = income.get_e(S, J, starting_age, ending_age, bin_weights, omega_SS)
mort_rate = 1-surv_rate
var_names = ['omega', 'g_n', 'omega_SS', 'children', 'surv_rate', 'e', 'mort_rate']
dictionary = {}
for key in var_names:
dictionary[key] = globals()[key]
pickle.dump(dictionary, open("OUTPUT/income_demo_vars.pkl", "w"))
else:
variables = pickle.load(open("OUTPUT/income_demo_vars.pkl", "r"))
for key in variables:
globals()[key] = variables[key]
chi_n_guess = np.array([47.12000874 , 22.22762421 , 14.34842241 , 10.67954008 , 8.41097278
, 7.15059004 , 6.46771332 , 5.85495452 , 5.46242013 , 5.00364263
def get_full_parameters():
'''
--------------------------------------------------------------------
Set exogenous model parameters including parameters determined
outside the model from other processes
--------------------------------------------------------------------
--------------------------------------------------------------------
'''
# Model Parameters
S = int(80)
J = int(7)
T = int(3 * S)
lambdas = np.array([.25, .25, .2, .1, .1, .09, .01])
starting_age = int(21)
ending_age = int(100)
E = int(
round(
float(S) * (float(starting_age) - 1) /
(float(ending_age - starting_age) + 1)))
beta_annual = .96
beta = beta_annual**(float(ending_age - starting_age + 1) / S)
sigma = 3.0
alpha = .35
Z = 1.0
delta_annual = .05
delta = 1 - (
(1 - delta_annual)**(float(ending_age - starting_age + 1) / S))
ltilde = 1.0
g_y_annual = 0.03
g_y = (1 + g_y_annual)**(float(ending_age - starting_age + 1) / S) - 1
# Elliptical disutility of labor parameters
b_ellipse = 25.6594
k_ellipse = -26.4902
upsilon = 3.0542
# Set parameters to generate effective tax rate parameters
beg_yr = int(2015)
end_yr = int(2024)
tpers = int(end_yr - beg_yr + 1)
numparams = int(10)
desc_data = False
graph_data = False
graph_est = False
dmtrgr_est = False
params_txfn = (starting_age, ending_age, beg_yr, end_yr, tpers, numparams,
desc_data, graph_data, graph_est, dmtrgr_est)
dict_tfparams = txfn.get_TaxFunParams(params_txfn)
# Simulation Parameters
MINIMIZER_TOL = 1e-14
MINIMIZER_OPTIONS = None
PLOT_TPI = True
maxiter = 250
mindist_SS = 1e-9
mindist_TPI = 1e-6
nu = .4
flag_graphs = False
# Calibration parameters
# These guesses are close to the calibrated values
chi_b_guess = np.array([2, 10, 90, 350, 1700, 22000, 120000])
chi_n_guess = np.array([
47.12000874, 22.22762421, 14.34842241, 10.67954008, 8.41097278,
7.15059004, 6.46771332, 5.85495452, 5.46242013, 5.00364263, 4.57322063,
4.53371545, 4.29828515, 4.10144524, 3.8617942, 3.57282, 3.47473172,
3.31111347, 3.04137299, 2.92616951, 2.58517969, 2.48761429, 2.21744847,
1.9577682, 1.66931057, 1.6878927, 1.63107201, 1.63390543, 1.5901486,
1.58143606, 1.58005578, 1.59073213, 1.60190899, 1.60001831, 1.67763741,
1.70451784, 1.85430468, 1.97291208, 1.97017228, 2.25518398, 2.43969757,
3.21870602, 4.18334822, 4.97772026, 6.37663164, 8.65075992, 9.46944758,
10.51634777, 12.13353793, 11.89186997, 12.07083882, 13.2992811,
14.07987878, 14.19951571, 14.97943562, 16.05601334, 16.42979341,
16.91576867, 17.62775142, 18.4885405, 19.10609921, 20.03988031,
20.86564363, 21.73645892, 22.6208256, 23.37786072, 24.38166073,
25.22395387, 26.21419653, 27.05246704, 27.86896121, 28.90029708,
29.83586775, 30.87563699, 31.91207845, 33.07449767, 34.27919965,
35.57195873, 36.95045988, 38.62308152
])
# Generate Income and Demographic parameters
omega, g_n_ss, omega_SS, surv_rate, rho, g_n_vector = get_omega(
S, T, starting_age, ending_age, E, flag_graphs)
e = get_e(S, J, starting_age, ending_age, lambdas, omega_SS, flag_graphs)
allvars = dict(locals())
return allvars
def get_reduced_parameters(baseline, guid, user_modifiable, metadata):
'''
--------------------------------------------------------------------
This function sets the parameters for the reduced model, which is
simplified to run more quickly for testing.
--------------------------------------------------------------------
INPUTS:
baseline = boolean, =True if baseline tax policy, =False if reform
guid = string, id for reform run
user_modifiable = boolean, =True if allow user modifiable parameters
metadata = boolean, =True if use metadata file for parameter
values (rather than what is entered in parameters below)
OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:
read_tax_func_estimate()
elliptical_u_est.estimation()
read_parameter_metadata()
OBJECTS CREATED WITHIN FUNCTION:
See parameters defined above
allvars = dictionary, dictionary with all parameters defined in this function
RETURNS: allvars
OUTPUT: None
--------------------------------------------------------------------
'''
# Model Parameters
starting_age = 40
ending_age = 50
S = int(ending_age-starting_age)
J = int(2)
T = int(2 * S)
BQ_dist = MVKDE(S, J ,proportion_matrix = None, filename = 'BQ_dist.txt', plot = False, bandwidth = .25)
BW = int(10)
lambdas = np.array([.50, .50])
E = int(starting_age * (S / float(ending_age - starting_age)))
beta_annual = .96 # Carroll (JME, 2009)
beta = beta_annual ** (float(ending_age - starting_age) / S)
sigma = 1.5 # value from Attanasio, Banks, Meghir and Weber (JEBS, 1999)
alpha = .35 # many use 0.33, but many find that capitals share is increasing (e.g. Elsby, Hobijn, and Sahin (BPEA, 2013))
Z = 1.0
delta_annual = .05 # approximately the value from Kehoe calibration exercise: http://www.econ.umn.edu/~tkehoe/classes/calibration-04.pdf
delta = 1 - ((1 - delta_annual) ** (float(ending_age - starting_age) / S))
ltilde = 1.0
g_y_annual = 0.03
g_y = (1 + g_y_annual)**(float(ending_age - starting_age) / S) - 1
# Ellipse parameters
frisch = 0.4 # Frisch elasticity consistent with Altonji (JPE, 1996) and Peterman (Econ Inquiry, 2016)
b_ellipse, upsilon = elliptical_u_est.estimation(frisch,ltilde)
k_ellipse = 0 # this parameter is just a level shifter in utlitiy - irrelevant for analysis
# Tax parameters:
# Income Tax Parameters
#### will call tax function estimation function here...
### do output such that each parameters is in a separate SxBW array
if baseline:
baseline_pckl = "TxFuncEst_baseline{}.pkl".format(guid)
estimate_file = os.path.join(TAX_ESTIMATE_PATH,
baseline_pckl)
print 'using baseline1 tax parameters'
dict_params = read_tax_func_estimate(estimate_file, baseline_pckl)
else:
policy_pckl = "TxFuncEst_policy{}.pkl".format(guid)
estimate_file = os.path.join(TAX_ESTIMATE_PATH,
policy_pckl)
print 'using policy1 tax parameters'
dict_params = read_tax_func_estimate(estimate_file, policy_pckl)
mean_income_data = dict_params['tfunc_avginc'][0]
etr_params = dict_params['tfunc_etr_params_S'][:S,:BW,:]
mtrx_params = dict_params['tfunc_mtrx_params_S'][:S,:BW,:]
mtry_params = dict_params['tfunc_mtry_params_S'][:S,:BW,:]
# To zero out income taxes, uncomment the following 3 lines:
# etr_params[:,:,6:] = 0.0
# mtrx_params[:,:,6:] = 0.0
# mtry_params[:,:,6:] = 0.0
# Wealth tax params
# These are non-calibrated values, h and m just need
# need to be nonzero to avoid errors. When p_wealth
# is zero, there is no wealth tax.
h_wealth = 0.1
m_wealth = 1.0
p_wealth = 0.0
# Bequest and Payroll Taxes
tau_bq = np.zeros(J)
tau_payroll = 0.15
retire = np.round(9.0 * S / 16.0) - 1
# Simulation Parameters
MINIMIZER_TOL = 1e-3
MINIMIZER_OPTIONS = {'maxiter': 1}
PLOT_TPI = False
maxiter = 10
mindist_SS = 1e-3
mindist_TPI = 1e-3 #1e-6
nu = .4
flag_graphs = False
# Calibration parameters
# These guesses are close to the calibrated values
chi_b_guess = np.array([1, 100000])
chi_n_guess = np.array([5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
# Generate Income and Demographic parameters
omega, g_n_ss, omega_SS, surv_rate, rho, g_n_vector = get_omega(
S, T, starting_age, ending_age, E, flag_graphs)
# omega, g_n_ss, omega_SS, surv_rate, rho, g_n_vector = get_pop_objs(
# E, S, T, 0, 100, 2015, flag_graphs)
e = np.array([[0.25, 1.25]] * 10)
allvars = dict(locals())
if user_modifiable:
allvars = {k:allvars[k] for k in USER_MODIFIABLE_PARAMS}
if metadata:
params_meta = read_parameter_metadata()
for k,v in allvars.iteritems():
params_meta[k]["value"] = v
allvars = params_meta
return allvars
def get_full_parameters():
# Model Parameters
S = 80
J = 7
T = int(2 * S)
lambdas = np.array([.25, .25, .2, .1, .1, .09, .01])
starting_age = 20
ending_age = 100
E = int(starting_age * (S / float(ending_age - starting_age)))
beta_annual = .96
beta = beta_annual**(float(ending_age - starting_age) / S)
sigma = 3.0
alpha = .35
Z = 1.0
delta_annual = .05
delta = 1 - ((1 - delta_annual)**(float(ending_age - starting_age) / S))
ltilde = 1.0
g_y_annual = 0.03
g_y = (1 + g_y_annual)**(float(ending_age - starting_age) / S) - 1
# Ellipse parameters
b_ellipse = 25.6594
k_ellipse = -26.4902
upsilon = 3.0542
# Tax parameters:
# Income Tax Parameters
mean_income_data = 84377.0
a_tax_income = 3.03452713268985e-06
b_tax_income = .222
c_tax_income = 133261.0
d_tax_income = .219
# Wealth tax params
# These are non-calibrated values, h and m just need
# need to be nonzero to avoid errors. When p_wealth
# is zero, there is no wealth tax.
h_wealth = 0.1
m_wealth = 1.0
p_wealth = 0.0
# Bequest and Payroll Taxes
tau_bq = np.zeros(J)
tau_payroll = 0.15
retire = np.round(9.0 * S / 16.0) - 1
# Simulation Parameters
MINIMIZER_TOL = 1e-14
MINIMIZER_OPTIONS = None
PLOT_TPI = True
maxiter = 250
mindist_SS = 1e-9
mindist_TPI = 1e-6
nu = .4
flag_graphs = False
# Calibration parameters
# These guesses are close to the calibrated values
chi_b_guess = np.array([2, 10, 90, 350, 1700, 22000, 120000])
chi_n_guess = np.array([
47.12000874, 22.22762421, 14.34842241, 10.67954008, 8.41097278,
7.15059004, 6.46771332, 5.85495452, 5.46242013, 5.00364263, 4.57322063,
4.53371545, 4.29828515, 4.10144524, 3.8617942, 3.57282, 3.47473172,
3.31111347, 3.04137299, 2.92616951, 2.58517969, 2.48761429, 2.21744847,
1.9577682, 1.66931057, 1.6878927, 1.63107201, 1.63390543, 1.5901486,
1.58143606, 1.58005578, 1.59073213, 1.60190899, 1.60001831, 1.67763741,
1.70451784, 1.85430468, 1.97291208, 1.97017228, 2.25518398, 2.43969757,
3.21870602, 4.18334822, 4.97772026, 6.37663164, 8.65075992, 9.46944758,
10.51634777, 12.13353793, 11.89186997, 12.07083882, 13.2992811,
14.07987878, 14.19951571, 14.97943562, 16.05601334, 16.42979341,
16.91576867, 17.62775142, 18.4885405, 19.10609921, 20.03988031,
20.86564363, 21.73645892, 22.6208256, 23.37786072, 24.38166073,
25.22395387, 26.21419653, 27.05246704, 27.86896121, 28.90029708,
29.83586775, 30.87563699, 31.91207845, 33.07449767, 34.27919965,
35.57195873, 36.95045988, 38.62308152
])
# Generate Income and Demographic parameters
omega, g_n_ss, omega_SS, surv_rate, rho, g_n_vector = get_omega(
S, T, starting_age, ending_age, E, flag_graphs)
e = get_e(S, J, starting_age, ending_age, lambdas, omega_SS, flag_graphs)
allvars = dict(locals())
return allvars
def get_reduced_parameters(baseline, guid, user_modifiable, metadata):
'''
--------------------------------------------------------------------
This function sets the parameters for the reduced model, which is
simplified to run more quickly for testing.
--------------------------------------------------------------------
INPUTS:
baseline = boolean, =True if baseline tax policy, =False if reform
guid = string, id for reform run
user_modifiable = boolean, =True if allow user modifiable parameters
metadata = boolean, =True if use metadata file for parameter
values (rather than what is entered in parameters below)
OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:
read_tax_func_estimate()
elliptical_u_est.estimation()
read_parameter_metadata()
OBJECTS CREATED WITHIN FUNCTION:
See parameters defined above
allvars = dictionary, dictionary with all parameters defined in this function
RETURNS: allvars
OUTPUT: None
--------------------------------------------------------------------
'''
# Model Parameters
starting_age = 40
ending_age = 50
S = int(ending_age-starting_age)
J = int(2)
T = int(2 * S)
BW = int(10)
lambdas = np.array([.50, .50])
E = int(starting_age * (S / float(ending_age - starting_age)))
beta_annual = .96 # Carroll (JME, 2009)
beta = beta_annual ** (float(ending_age - starting_age) / S)
sigma = 1.5 # value from Attanasio, Banks, Meghir and Weber (JEBS, 1999)
alpha = .35 # many use 0.33, but many find that capitals share is increasing (e.g. Elsby, Hobijn, and Sahin (BPEA, 2013))
Z = 1.0
delta_annual = .05 # approximately the value from Kehoe calibration exercise: http://www.econ.umn.edu/~tkehoe/classes/calibration-04.pdf
delta = 1 - ((1 - delta_annual) ** (float(ending_age - starting_age) / S))
ltilde = 1.0
g_y_annual = 0.03
g_y = (1 + g_y_annual)**(float(ending_age - starting_age) / S) - 1
# Ellipse parameters
frisch = 0.4 # Frisch elasticity consistent with Altonji (JPE, 1996) and Peterman (Econ Inquiry, 2016)
b_ellipse, upsilon = elliptical_u_est.estimation(frisch,ltilde)
k_ellipse = 0 # this parameter is just a level shifter in utlitiy - irrelevant for analysis
# Tax parameters:
# Income Tax Parameters
#### will call tax function estimation function here...
### do output such that each parameters is in a separate SxBW array
if baseline:
baseline_pckl = "TxFuncEst_baseline{}.pkl".format(guid)
estimate_file = os.path.join(TAX_ESTIMATE_PATH,
baseline_pckl)
print 'using baseline1 tax parameters'
dict_params = read_tax_func_estimate(estimate_file, baseline_pckl)
else:
policy_pckl = "TxFuncEst_policy{}.pkl".format(guid)
estimate_file = os.path.join(TAX_ESTIMATE_PATH,
policy_pckl)
print 'using policy1 tax parameters'
dict_params = read_tax_func_estimate(estimate_file, policy_pckl)
mean_income_data = dict_params['tfunc_avginc'][0]
etr_params = dict_params['tfunc_etr_params_S'][:S,:BW,:]
mtrx_params = dict_params['tfunc_mtrx_params_S'][:S,:BW,:]
mtry_params = dict_params['tfunc_mtry_params_S'][:S,:BW,:]
# To zero out income taxes, uncomment the following 3 lines:
# etr_params[:,:,6:] = 0.0
# mtrx_params[:,:,6:] = 0.0
# mtry_params[:,:,6:] = 0.0
# Wealth tax params
# These are non-calibrated values, h and m just need
# need to be nonzero to avoid errors. When p_wealth
# is zero, there is no wealth tax.
h_wealth = 0.1
m_wealth = 1.0
p_wealth = 0.0
# Bequest and Payroll Taxes
tau_bq = np.zeros(J)
tau_payroll = 0.15
retire = np.round(9.0 * S / 16.0) - 1
# Simulation Parameters
MINIMIZER_TOL = 1e-3
MINIMIZER_OPTIONS = {'maxiter': 1}
PLOT_TPI = False
maxiter = 10
mindist_SS = 1e-3
mindist_TPI = 1e-3 #1e-6
nu = .4
flag_graphs = False
# Calibration parameters
# These guesses are close to the calibrated values
chi_b_guess = np.array([1, 100000])
chi_n_guess = np.array([5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
# Generate Income and Demographic parameters
omega, g_n_ss, omega_SS, surv_rate, rho, g_n_vector = get_omega(
S, T, starting_age, ending_age, E, flag_graphs)
# omega, g_n_ss, omega_SS, surv_rate, rho, g_n_vector = get_pop_objs(
# E, S, T, 0, 100, 2015, flag_graphs)
e = np.array([[0.25, 1.25]] * 10)
allvars = dict(locals())
if user_modifiable:
allvars = {k:allvars[k] for k in USER_MODIFIABLE_PARAMS}
if metadata:
params_meta = read_parameter_metadata()
for k,v in allvars.iteritems():
params_meta[k]["value"] = v
allvars = params_meta
return allvars
def get_full_parameters(baseline, guid, user_modifiable, metadata):
'''
--------------------------------------------------------------------
This function sets the parameters for the full model.
--------------------------------------------------------------------
INPUTS:
baseline = boolean, =True if baseline tax policy, =False if reform
guid = string, id for reform run
user_modifiable = boolean, =True if allow user modifiable parameters
metadata = boolean, =True if use metadata file for parameter
values (rather than what is entered in parameters below)
OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:
read_tax_func_estimate()
elliptical_u_est.estimation()
read_parameter_metadata()
OBJECTS CREATED WITHIN FUNCTION:
See parameters defined above
allvars = dictionary, dictionary with all parameters defined in this function
RETURNS: allvars
OUTPUT: None
--------------------------------------------------------------------
'''
# Model Parameters
S = int(80)
J = int(7)
T = int(2 * S)
BW = int(10)
lambdas = np.array([.25, .25, .2, .1, .1, .09, .01])
starting_age = 20
ending_age = 100
E = int(starting_age * (S / float(ending_age - starting_age)))
beta_annual = .96 # Carroll (JME, 2009)
beta = beta_annual ** (float(ending_age - starting_age) / S)
sigma = 1.5 # value from Attanasio, Banks, Meghir and Weber (JEBS, 1999)
alpha = .35 # many use 0.33, but many find that capitals share is increasing (e.g. Elsby, Hobijn, and Sahin (BPEA, 2013))
Z = 1.0
delta_annual = .05 # approximately the value from Kehoe calibration exercise: http://www.econ.umn.edu/~tkehoe/classes/calibration-04.pdf
delta = 1 - ((1 - delta_annual) ** (float(ending_age - starting_age) / S))
ltilde = 1.0
g_y_annual = 0.03
g_y = (1 + g_y_annual)**(float(ending_age - starting_age) / S) - 1
# Ellipse parameters
frisch = 0.4 # Frisch elasticity consistent with Altonji (JPE, 1996) and Peterman (Econ Inquiry, 2016)
b_ellipse, upsilon = elliptical_u_est.estimation(frisch,ltilde)
k_ellipse = 0 # this parameter is just a level shifter in utlitiy - irrelevant for analysis
# Tax parameters:
# Income Tax Parameters
# will call tax function estimation function here...
# do output such that each parameters is in a separate SxBW array
# read in estimated parameters
print 'baselines is:', baseline
if baseline:
baseline_pckl = "TxFuncEst_baseline{}.pkl".format(guid)
estimate_file = os.path.join(TAX_ESTIMATE_PATH,
baseline_pckl)
print 'using baseline2 tax parameters'
dict_params = read_tax_func_estimate(estimate_file, baseline_pckl)
else:
policy_pckl = "TxFuncEst_policy{}.pkl".format(guid)
estimate_file = os.path.join(TAX_ESTIMATE_PATH,
policy_pckl)
print 'using policy2 tax parameters'
dict_params = read_tax_func_estimate(estimate_file, policy_pckl)
mean_income_data = dict_params['tfunc_avginc'][0]
# etr_params = dict_params['tfunc_etr_params_S'][:S,:BW,:]
# mtrx_params = dict_params['tfunc_mtrx_params_S'][:S,:BW,:]
# mtry_params = dict_params['tfunc_mtry_params_S'][:S,:BW,:]
# set etrs and mtrs to constant rates over income/age
etr_params = np.zeros((S,BW,10))
mtrx_params = np.zeros((S,BW,10))
mtry_params = np.zeros((S,BW,10))
etr_params[:,:,7] = dict_params['tfunc_avg_etr']
mtrx_params[:,:,7] = dict_params['tfunc_avg_mtrx']
mtry_params[:,:,7] = dict_params['tfunc_avg_mtry']
etr_params[:,:,9] = dict_params['tfunc_avg_etr']
mtrx_params[:,:,9] = dict_params['tfunc_avg_mtrx']
mtry_params[:,:,9] = dict_params['tfunc_avg_mtry']
etr_params[:,:,5] = 1.0
mtrx_params[:,:,5] = 1.0
mtry_params[:,:,5] = 1.0
# To zero out income taxes, uncomment the following 3 lines:
# etr_params[:,:,6:] = 0.0
# mtrx_params[:,:,6:] = 0.0
# mtry_params[:,:,6:] = 0.0
# Wealth tax params
# These are non-calibrated values, h and m just need
# need to be nonzero to avoid errors. When p_wealth
# is zero, there is no wealth tax.
h_wealth = 0.1
m_wealth = 1.0
p_wealth = 0.0
# Bequest and Payroll Taxes
tau_bq = np.zeros(J)
tau_payroll = 0.0 #0.15 # were are inluding payroll taxes in tax functions for now
retire = np.round(9.0 * S / 16.0) - 1
# Simulation Parameters
MINIMIZER_TOL = 1e-14
MINIMIZER_OPTIONS = None
PLOT_TPI = False
maxiter = 250
mindist_SS = 1e-9
mindist_TPI = 2e-5
nu = .4
flag_graphs = False
# Calibration parameters
# These guesses are close to the calibrated values
chi_b_guess = np.ones((J,)) * 80.0
#chi_b_guess = np.array([0.7, 0.7, 1.0, 1.2, 1.2, 1.2, 1.4])
#chi_b_guess = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 10.0])
#chi_b_guess = np.array([5, 10, 90, 250, 250, 250, 250])
#chi_b_guess = np.array([2, 10, 90, 350, 1700, 22000, 120000])
chi_n_guess = np.array([38.12000874, 33.22762421, 25.34842241, 26.67954008, 24.41097278,
23.15059004, 22.46771332, 21.85495452, 21.46242013, 22.00364263,
21.57322063, 21.53371545, 21.29828515, 21.10144524, 20.8617942,
20.57282, 20.47473172, 20.31111347, 19.04137299, 18.92616951,
20.58517969, 20.48761429, 20.21744847, 19.9577682, 19.66931057,
19.6878927, 19.63107201, 19.63390543, 19.5901486, 19.58143606,
19.58005578, 19.59073213, 19.60190899, 19.60001831, 21.67763741,
21.70451784, 21.85430468, 21.97291208, 21.97017228, 22.25518398,
22.43969757, 23.21870602, 24.18334822, 24.97772026, 26.37663164,
29.65075992, 30.46944758, 31.51634777, 33.13353793, 32.89186997,
38.07083882, 39.2992811, 40.07987878, 35.19951571, 35.97943562,
37.05601334, 37.42979341, 37.91576867, 38.62775142, 39.4885405,
37.10609921, 40.03988031, 40.86564363, 41.73645892, 42.6208256,
43.37786072, 45.38166073, 46.22395387, 50.21419653, 51.05246704,
53.86896121, 53.90029708, 61.83586775, 64.87563699, 66.91207845,
68.07449767, 71.27919965, 73.57195873, 74.95045988, 76.62308152])
# Generate Income and Demographic parameters
omega_hat, g_n_ss, omega_SS, surv_rate, rho, g_n_vector = get_omega(
S, T, starting_age, ending_age, E, flag_graphs)
# omega, g_n_ss, omega_SS, surv_rate, rho, g_n_vector = get_pop_objs(
# E, S, T, 0, 100, 2015, flag_graphs)
# print 'Differences:'
# print 'omega diffs: ', (np.absolute(omega-omega2)).max()
# print 'g_n', g_n_ss, g_n_ss2
# print 'omega SS diffs: ', (np.absolute(omega_SS-omega_SS2)).max()
# print 'surv diffs: ', (np.absolute(surv_rate- surv_rate2)).max()
# print 'mort diffs: ', (np.absolute(rho- rho2)).max()
# print 'g_n_TP diffs: ', (np.absolute(g_n_vector- g_n_vector2)).max()
# quit()
#print 'omega_SS shape: ', omega_SS.shape
g_n_ss = 0.0
surv_rate1 = np.ones((S,))# prob start at age S
surv_rate1[1:] = np.cumprod(surv_rate[:-1], dtype=float)
omega_SS = np.ones(S)*surv_rate1# number of each age alive at any time
omega_SS = omega_SS/omega_SS.sum()
omega = np.tile(np.reshape(omega_SS,(1,S)),(T+S,1))
g_n_vector = np.tile(g_n_ss,(T+S,))
e_hetero = get_e(S, J, starting_age, ending_age, lambdas, omega_SS, flag_graphs)
e = np.tile(((e_hetero*lambdas).sum(axis=1)).reshape(S,1),(1,J))
e /= (e * omega_SS.reshape(S, 1)* lambdas.reshape(1, J)).sum()
allvars = dict(locals())
if user_modifiable:
allvars = {k:allvars[k] for k in USER_MODIFIABLE_PARAMS}
if metadata:
params_meta = read_parameter_metadata()
for k,v in allvars.iteritems():
params_meta[k]["value"] = v
allvars = params_meta
return allvars
def get_full_parameters(baseline, guid, user_modifiable, metadata):
# Model Parameters
S = int(80)
J = int(7)
T = int(2 * S)
BW = int(10)
lambdas = np.array([.25, .25, .2, .1, .1, .09, .01])
starting_age = 20
ending_age = 100
E = int(starting_age * (S / float(ending_age - starting_age)))
beta_annual = .96 # Carroll (JME, 2009)
beta = beta_annual ** (float(ending_age - starting_age) / S)
sigma = 1.5 # value from Attanasio, Banks, Meghir and Weber (JEBS, 1999)
alpha = .35 # many use 0.33, but many find that capitals share is increasing (e.g. Elsby, Hobijn, and Sahin (BPEA, 2013))
Z = 1.0
delta_annual = .05 # approximately the value from Kehoe calibration exercise: http://www.econ.umn.edu/~tkehoe/classes/calibration-04.pdf
delta = 1 - ((1 - delta_annual) ** (float(ending_age - starting_age) / S))
ltilde = 1.0
g_y_annual = 0.03
g_y = (1 + g_y_annual)**(float(ending_age - starting_age) / S) - 1
# Ellipse parameters
frisch = 0.4 # Frisch elasticity consistent with Altonji (JPE, 1996) and Peterman (Econ Inquiry, 2016)
b_ellipse, upsilon = elliptical_u_est.estimation(frisch,ltilde)
k_ellipse = 0 # this parameter is just a level shifter in utlitiy - irrelevant for analysis
# Tax parameters:
# Income Tax Parameters
# will call tax function estimation function here...
# do output such that each parameters is in a separate SxBW array
# read in estimated parameters
print 'baselines is:', baseline
if baseline:
baseline_pckl = "TxFuncEst_baseline{}.pkl".format(guid)
estimate_file = os.path.join(TAX_ESTIMATE_PATH,
baseline_pckl)
print 'using baseline2 tax parameters'
dict_params = read_tax_func_estimate(estimate_file, baseline_pckl)
else:
policy_pckl = "TxFuncEst_policy{}.pkl".format(guid)
estimate_file = os.path.join(TAX_ESTIMATE_PATH,
policy_pckl)
print 'using policy2 tax parameters'
dict_params = read_tax_func_estimate(estimate_file, policy_pckl)
mean_income_data = dict_params['tfunc_avginc'][0]
etr_params = dict_params['tfunc_etr_params_S'][:S,:BW,:]
mtrx_params = dict_params['tfunc_mtrx_params_S'][:S,:BW,:]
mtry_params = dict_params['tfunc_mtry_params_S'][:S,:BW,:]
# To zero out income taxes, uncomment the following 3 lines:
# etr_params[:,:,6:] = 0.0
# mtrx_params[:,:,6:] = 0.0
# mtry_params[:,:,6:] = 0.0
# Wealth tax params
# These are non-calibrated values, h and m just need
# need to be nonzero to avoid errors. When p_wealth
# is zero, there is no wealth tax.
h_wealth = 0.1
m_wealth = 1.0
p_wealth = 0.0
# Bequest and Payroll Taxes
tau_bq = np.zeros(J)
tau_payroll = 0.0 #0.15 # were are inluding payroll taxes in tax functions for now
retire = np.round(9.0 * S / 16.0) - 1
# Simulation Parameters
MINIMIZER_TOL = 1e-14
MINIMIZER_OPTIONS = None
PLOT_TPI = False
maxiter = 250
mindist_SS = 1e-9
mindist_TPI = 2e-5
nu = .4
flag_graphs = False
# Calibration parameters
# These guesses are close to the calibrated values
#chi_b_guess = np.array([0.7, 0.7, 1.0, 1.2, 1.2, 1.2, 1.4])
chi_b_guess = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
chi_n_guess = np.array([38.12000874, 33.22762421, 25.34842241, 26.67954008, 24.41097278,
23.15059004, 22.46771332, 21.85495452, 21.46242013, 22.00364263,
21.57322063, 21.53371545, 21.29828515, 21.10144524, 20.8617942,
20.57282, 20.47473172, 20.31111347, 19.04137299, 18.92616951,
20.58517969, 20.48761429, 20.21744847, 19.9577682, 19.66931057,
19.6878927, 19.63107201, 19.63390543, 19.5901486, 19.58143606,
19.58005578, 19.59073213, 19.60190899, 19.60001831, 21.67763741,
21.70451784, 21.85430468, 21.97291208, 21.97017228, 22.25518398,
22.43969757, 23.21870602, 24.18334822, 24.97772026, 26.37663164,
29.65075992, 30.46944758, 31.51634777, 33.13353793, 32.89186997,
38.07083882, 39.2992811, 40.07987878, 35.19951571, 35.97943562,
37.05601334, 37.42979341, 37.91576867, 38.62775142, 39.4885405,
37.10609921, 40.03988031, 40.86564363, 41.73645892, 42.6208256,
43.37786072, 45.38166073, 46.22395387, 50.21419653, 51.05246704,
53.86896121, 53.90029708, 61.83586775, 64.87563699, 66.91207845,
68.07449767, 71.27919965, 73.57195873, 74.95045988, 76.62308152])
# Generate Income and Demographic parameters
omega, g_n_ss, omega_SS, surv_rate, rho, g_n_vector = get_omega(
S, T, starting_age, ending_age, E, flag_graphs)
e = get_e(S, J, starting_age, ending_age, lambdas, omega_SS, flag_graphs)
allvars = dict(locals())
if user_modifiable:
allvars = {k:allvars[k] for k in USER_MODIFIABLE_PARAMS}
if metadata:
params_meta = read_parameter_metadata()
for k,v in allvars.iteritems():
params_meta[k]["value"] = v
allvars = params_meta
return allvars
3.04137299, 2.92616951, 2.58517969, 2.48761429, 2.21744847, 1.9577682,
1.66931057, 1.6878927, 1.63107201, 1.63390543, 1.5901486, 1.58143606,
1.58005578, 1.59073213, 1.60190899, 1.60001831, 1.67763741, 1.70451784,
1.85430468, 1.97291208, 1.97017228, 2.25518398, 2.43969757, 3.21870602,
4.18334822, 4.97772026, 6.37663164, 8.65075992, 9.46944758, 10.51634777,
12.13353793, 11.89186997, 12.07083882, 13.2992811, 14.07987878,
14.19951571, 14.97943562, 16.05601334, 16.42979341, 16.91576867,
17.62775142, 18.4885405, 19.10609921, 20.03988031, 20.86564363,
21.73645892, 22.6208256, 23.37786072, 24.38166073, 25.22395387,
26.21419653, 27.05246704, 27.86896121, 28.90029708, 29.83586775,
30.87563699, 31.91207845, 33.07449767, 34.27919965, 35.57195873,
36.95045988, 38.62308152
])
# Demographic and Ability variables
omega, g_n_ss, omega_SS, surv_rate, rho, g_n_vector = demographics.get_omega(
S, T, starting_age, ending_age, E, flag_graphs)
e = income.get_e(S, J, starting_age, ending_age, lambdas, omega_SS,
flag_graphs)
# List of parameter names that will not be changing (unless we decide to
# change them for a tax experiment)
param_names = [
'S', 'J', 'T', 'lambdas', 'starting_age', 'ending_age', 'beta', 'sigma',
'alpha', 'nu', 'Z', 'delta', 'E', 'ltilde', 'g_y', 'maxiter', 'mindist_SS',
'mindist_TPI', 'b_ellipse', 'k_ellipse', 'upsilon', 'a_tax_income',
'chi_b_guess', 'chi_n_guess', 'b_tax_income', 'c_tax_income',
'd_tax_income', 'tau_payroll', 'tau_bq', 'calibrate_model', 'retire',
'mean_income_data', 'g_n_vector', 'h_wealth', 'p_wealth', 'm_wealth',
'get_baseline', 'omega', 'g_n_ss', 'omega_SS', 'surv_rate', 'e', 'rho'
]
'''
def get_full_parameters(baseline, guid, user_modifiable, metadata):
# Model Parameters
S = int(80)
J = int(7)
T = int(2 * S)
BW = int(10)
lambdas = np.array([.25, .25, .2, .1, .1, .09, .01])
starting_age = 20
ending_age = 100
E = int(starting_age * (S / float(ending_age - starting_age)))
beta_annual = .96 # Carroll (JME, 2009)
beta = beta_annual**(float(ending_age - starting_age) / S)
sigma = 1.5 # value from Attanasio, Banks, Meghir and Weber (JEBS, 1999)
alpha = .35 # many use 0.33, but many find that capitals share is increasing (e.g. Elsby, Hobijn, and Sahin (BPEA, 2013))
Z = 1.0
delta_annual = .05 # approximately the value from Kehoe calibration exercise: http://www.econ.umn.edu/~tkehoe/classes/calibration-04.pdf
delta = 1 - ((1 - delta_annual)**(float(ending_age - starting_age) / S))
ltilde = 1.0
g_y_annual = 0.03
g_y = (1 + g_y_annual)**(float(ending_age - starting_age) / S) - 1
# Ellipse parameters
frisch = 0.4 # Frisch elasticity consistent with Altonji (JPE, 1996) and Peterman (Econ Inquiry, 2016)
b_ellipse, upsilon = elliptical_u_est.estimation(frisch, ltilde)
k_ellipse = 0 # this parameter is just a level shifter in utlitiy - irrelevant for analysis
# Tax parameters:
# Income Tax Parameters
# will call tax function estimation function here...
# do output such that each parameters is in a separate SxBW array
# read in estimated parameters
print 'baselines is:', baseline
if baseline:
baseline_pckl = "TxFuncEst_baseline{}.pkl".format(guid)
estimate_file = os.path.join(TAX_ESTIMATE_PATH, baseline_pckl)
print 'using baseline2 tax parameters'
dict_params = read_tax_func_estimate(estimate_file, baseline_pckl)
else:
policy_pckl = "TxFuncEst_policy{}.pkl".format(guid)
estimate_file = os.path.join(TAX_ESTIMATE_PATH, policy_pckl)
print 'using policy2 tax parameters'
dict_params = read_tax_func_estimate(estimate_file, policy_pckl)
mean_income_data = dict_params['tfunc_avginc'][0]
etr_params = dict_params['tfunc_etr_params_S'][:S, :BW, :]
mtrx_params = dict_params['tfunc_mtrx_params_S'][:S, :BW, :]
mtry_params = dict_params['tfunc_mtry_params_S'][:S, :BW, :]
# To zero out income taxes, uncomment the following 3 lines:
# etr_params[:,:,6:] = 0.0
# mtrx_params[:,:,6:] = 0.0
# mtry_params[:,:,6:] = 0.0
# Wealth tax params
# These are non-calibrated values, h and m just need
# need to be nonzero to avoid errors. When p_wealth
# is zero, there is no wealth tax.
h_wealth = 0.1
m_wealth = 1.0
p_wealth = 0.0
# Bequest and Payroll Taxes
tau_bq = np.zeros(J)
tau_payroll = 0.0 #0.15 # were are inluding payroll taxes in tax functions for now
retire = np.round(9.0 * S / 16.0) - 1
# Simulation Parameters
MINIMIZER_TOL = 1e-14
MINIMIZER_OPTIONS = None
PLOT_TPI = False
maxiter = 250
mindist_SS = 1e-9
mindist_TPI = 2e-5
nu = .4
flag_graphs = False
# Calibration parameters
# These guesses are close to the calibrated values
#chi_b_guess = np.array([0.7, 0.7, 1.0, 1.2, 1.2, 1.2, 1.4])
chi_b_guess = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
chi_n_guess = np.array([
38.12000874, 33.22762421, 25.34842241, 26.67954008, 24.41097278,
23.15059004, 22.46771332, 21.85495452, 21.46242013, 22.00364263,
21.57322063, 21.53371545, 21.29828515, 21.10144524, 20.8617942,
20.57282, 20.47473172, 20.31111347, 19.04137299, 18.92616951,
20.58517969, 20.48761429, 20.21744847, 19.9577682, 19.66931057,
19.6878927, 19.63107201, 19.63390543, 19.5901486, 19.58143606,
19.58005578, 19.59073213, 19.60190899, 19.60001831, 21.67763741,
21.70451784, 21.85430468, 21.97291208, 21.97017228, 22.25518398,
22.43969757, 23.21870602, 24.18334822, 24.97772026, 26.37663164,
29.65075992, 30.46944758, 31.51634777, 33.13353793, 32.89186997,
38.07083882, 39.2992811, 40.07987878, 35.19951571, 35.97943562,
37.05601334, 37.42979341, 37.91576867, 38.62775142, 39.4885405,
37.10609921, 40.03988031, 40.86564363, 41.73645892, 42.6208256,
43.37786072, 45.38166073, 46.22395387, 50.21419653, 51.05246704,
53.86896121, 53.90029708, 61.83586775, 64.87563699, 66.91207845,
68.07449767, 71.27919965, 73.57195873, 74.95045988, 76.62308152
])
# Generate Income and Demographic parameters
omega, g_n_ss, omega_SS, surv_rate, rho, g_n_vector = get_omega(
S, T, starting_age, ending_age, E, flag_graphs)
e = get_e(S, J, starting_age, ending_age, lambdas, omega_SS, flag_graphs)
allvars = dict(locals())
if user_modifiable:
allvars = {k: allvars[k] for k in USER_MODIFIABLE_PARAMS}
if metadata:
params_meta = read_parameter_metadata()
for k, v in allvars.iteritems():
params_meta[k]["value"] = v
allvars = params_meta
return allvars
------------------------------------------------------------------------
Generate income and demographic parameters
------------------------------------------------------------------------
e = S x J matrix of age dependent possible working abilities
e_s
omega = T x S x J array of demographics
g_n = steady state population growth rate
omega_SS = steady state population distribution
surv_rate = S x 1 array of survival rates
rho = S x 1 array of mortality rates
------------------------------------------------------------------------
'''
if SS_stage == 'first_run_for_guesses':
# These values never change, so only run it once
omega, g_n, omega_SS, surv_rate = demographics.get_omega(
S, J, T, lambdas, starting_age, ending_age, E)
e = income.get_e(S, J, starting_age, ending_age, lambdas, omega_SS)
rho = 1-surv_rate
var_names = ['omega', 'g_n', 'omega_SS', 'surv_rate', 'e', 'rho']
dictionary = {}
for key in var_names:
dictionary[key] = globals()[key]
pickle.dump(dictionary, open("OUTPUT/income_demo_vars.pkl", "w"))
else:
variables = pickle.load(open("OUTPUT/income_demo_vars.pkl", "r"))
for key in variables:
globals()[key] = variables[key]
chi_n_guess = np.array([47.12000874 , 22.22762421 , 14.34842241 , 10.67954008 , 8.41097278
, 7.15059004 , 6.46771332 , 5.85495452 , 5.46242013 , 5.00364263
def get_full_parameters(baseline, guid, user_modifiable, metadata):
'''
--------------------------------------------------------------------
This function sets the parameters for the full model.
--------------------------------------------------------------------
INPUTS:
baseline = boolean, =True if baseline tax policy, =False if reform
guid = string, id for reform run
user_modifiable = boolean, =True if allow user modifiable parameters
metadata = boolean, =True if use metadata file for parameter
values (rather than what is entered in parameters below)
OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:
read_tax_func_estimate()
elliptical_u_est.estimation()
read_parameter_metadata()
OBJECTS CREATED WITHIN FUNCTION:
See parameters defined above
allvars = dictionary, dictionary with all parameters defined in this function
RETURNS: allvars
OUTPUT: None
--------------------------------------------------------------------
'''
# Model Parameters
S = int(80)
J = int(7)
T = int(2 * S)
BQ_dist = MVKDE(S, J ,proportion_matrix = None, filename = 'BQ_dist.txt', plot = False, bandwidth = .25)
BW = int(10)
lambdas = np.array([.25, .25, .2, .1, .1, .09, .01])
starting_age = 20
ending_age = 100
E = int(starting_age * (S / float(ending_age - starting_age)))
beta_annual = .96 # Carroll (JME, 2009)
beta = beta_annual ** (float(ending_age - starting_age) / S)
sigma = 1.5 # value from Attanasio, Banks, Meghir and Weber (JEBS, 1999)
alpha = .35 # many use 0.33, but many find that capitals share is increasing (e.g. Elsby, Hobijn, and Sahin (BPEA, 2013))
Z = 1.0
delta_annual = .05 # approximately the value from Kehoe calibration exercise: http://www.econ.umn.edu/~tkehoe/classes/calibration-04.pdf
delta = 1 - ((1 - delta_annual) ** (float(ending_age - starting_age) / S))
ltilde = 1.0
g_y_annual = 0.03
g_y = (1 + g_y_annual)**(float(ending_age - starting_age) / S) - 1
# Ellipse parameters
frisch = 0.4 # Frisch elasticity consistent with Altonji (JPE, 1996) and Peterman (Econ Inquiry, 2016)
b_ellipse, upsilon = elliptical_u_est.estimation(frisch,ltilde)
k_ellipse = 0 # this parameter is just a level shifter in utlitiy - irrelevant for analysis
# Tax parameters:
# Income Tax Parameters
# will call tax function estimation function here...
# do output such that each parameters is in a separate SxBW array
# read in estimated parameters
print 'baselines is:', baseline
if baseline:
baseline_pckl = "TxFuncEst_baseline{}.pkl".format(guid)
estimate_file = os.path.join(TAX_ESTIMATE_PATH,
baseline_pckl)
print 'using baseline2 tax parameters'
dict_params = read_tax_func_estimate(estimate_file, baseline_pckl)
else:
policy_pckl = "TxFuncEst_policy{}.pkl".format(guid)
estimate_file = os.path.join(TAX_ESTIMATE_PATH,
policy_pckl)
print 'using policy2 tax parameters'
dict_params = read_tax_func_estimate(estimate_file, policy_pckl)
mean_income_data = dict_params['tfunc_avginc'][0]
# etr_params = dict_params['tfunc_etr_params_S'][:S,:BW,:]
# mtrx_params = dict_params['tfunc_mtrx_params_S'][:S,:BW,:]
# mtry_params = dict_params['tfunc_mtry_params_S'][:S,:BW,:]
# set etrs and mtrs to constant rates over income/age
etr_params = np.zeros((S,BW,10))
mtrx_params = np.zeros((S,BW,10))
mtry_params = np.zeros((S,BW,10))
etr_params[:,:,7] = dict_params['tfunc_avg_etr']
mtrx_params[:,:,7] = dict_params['tfunc_avg_mtrx']
mtry_params[:,:,7] = dict_params['tfunc_avg_mtry']
etr_params[:,:,9] = dict_params['tfunc_avg_etr']
mtrx_params[:,:,9] = dict_params['tfunc_avg_mtrx']
mtry_params[:,:,9] = dict_params['tfunc_avg_mtry']
etr_params[:,:,5] = 1.0
mtrx_params[:,:,5] = 1.0
mtry_params[:,:,5] = 1.0
# To zero out income taxes, uncomment the following 3 lines:
# etr_params[:,:,6:] = 0.0
# mtrx_params[:,:,6:] = 0.0
# mtry_params[:,:,6:] = 0.0
# Wealth tax params
# These are non-calibrated values, h and m just need
# need to be nonzero to avoid errors. When p_wealth
# is zero, there is no wealth tax.
h_wealth = 0.1
m_wealth = 1.0
p_wealth = 0.0
# Bequest and Payroll Taxes
tau_bq = np.zeros(J)
tau_payroll = 0.0 #0.15 # were are inluding payroll taxes in tax functions for now
retire = np.round(9.0 * S / 16.0) - 1
# Simulation Parameters
MINIMIZER_TOL = 1e-14
MINIMIZER_OPTIONS = None
PLOT_TPI = False
maxiter = 250
mindist_SS = 1e-9
mindist_TPI = 2e-5
nu = .4
flag_graphs = False
# Calibration parameters
# These guesses are close to the calibrated values
chi_b_guess = np.ones((J,)) * 80.0
#chi_b_guess = np.array([0.7, 0.7, 1.0, 1.2, 1.2, 1.2, 1.4])
#chi_b_guess = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 10.0])
#chi_b_guess = np.array([5, 10, 90, 250, 250, 250, 250])
#chi_b_guess = np.array([2, 10, 90, 350, 1700, 22000, 120000])
chi_n_guess = np.array([38.12000874, 33.22762421, 25.34842241, 26.67954008, 24.41097278,
23.15059004, 22.46771332, 21.85495452, 21.46242013, 22.00364263,
21.57322063, 21.53371545, 21.29828515, 21.10144524, 20.8617942,
20.57282, 20.47473172, 20.31111347, 19.04137299, 18.92616951,
20.58517969, 20.48761429, 20.21744847, 19.9577682, 19.66931057,
19.6878927, 19.63107201, 19.63390543, 19.5901486, 19.58143606,
19.58005578, 19.59073213, 19.60190899, 19.60001831, 21.67763741,
21.70451784, 21.85430468, 21.97291208, 21.97017228, 22.25518398,
22.43969757, 23.21870602, 24.18334822, 24.97772026, 26.37663164,
29.65075992, 30.46944758, 31.51634777, 33.13353793, 32.89186997,
38.07083882, 39.2992811, 40.07987878, 35.19951571, 35.97943562,
37.05601334, 37.42979341, 37.91576867, 38.62775142, 39.4885405,
37.10609921, 40.03988031, 40.86564363, 41.73645892, 42.6208256,
43.37786072, 45.38166073, 46.22395387, 50.21419653, 51.05246704,
53.86896121, 53.90029708, 61.83586775, 64.87563699, 66.91207845,
68.07449767, 71.27919965, 73.57195873, 74.95045988, 76.62308152])
# Generate Income and Demographic parameters
omega_hat, g_n_ss, omega_SS, surv_rate, rho, g_n_vector = get_omega(
S, T, starting_age, ending_age, E, flag_graphs)
# omega, g_n_ss, omega_SS, surv_rate, rho, g_n_vector = get_pop_objs(
# E, S, T, 0, 100, 2015, flag_graphs)
# print 'Differences:'
# print 'omega diffs: ', (np.absolute(omega-omega2)).max()
# print 'g_n', g_n_ss, g_n_ss2
# print 'omega SS diffs: ', (np.absolute(omega_SS-omega_SS2)).max()
# print 'surv diffs: ', (np.absolute(surv_rate- surv_rate2)).max()
# print 'mort diffs: ', (np.absolute(rho- rho2)).max()
# print 'g_n_TP diffs: ', (np.absolute(g_n_vector- g_n_vector2)).max()
# quit()
#print 'omega_SS shape: ', omega_SS.shape
g_n_ss = 0.0
surv_rate1 = np.ones((S,))# prob start at age S
surv_rate1[1:] = np.cumprod(surv_rate[:-1], dtype=float)
omega_SS = np.ones(S)*surv_rate1# number of each age alive at any time
omega_SS = omega_SS/omega_SS.sum()
omega = np.tile(np.reshape(omega_SS,(1,S)),(T+S,1))
g_n_vector = np.tile(g_n_ss,(T+S,))
e_hetero = get_e(S, J, starting_age, ending_age, lambdas, omega_SS, flag_graphs)
e = np.tile(((e_hetero*lambdas).sum(axis=1)).reshape(S,1),(1,J))
e /= (e * omega_SS.reshape(S, 1)* lambdas.reshape(1, J)).sum()
allvars = dict(locals())
if user_modifiable:
allvars = {k:allvars[k] for k in USER_MODIFIABLE_PARAMS}
if metadata:
params_meta = read_parameter_metadata()
for k,v in allvars.iteritems():
params_meta[k]["value"] = v
allvars = params_meta
return allvars
S = 80
tot_per = E + S
T = 320
#population parameters
fert_graph = False
mort_graph = False
imm_graph = False
omega_graph = False
# df_mort = pd.read_csv('mort_rates2011.csv')
# df_pop = pd.read_csv('pop_data.csv')
fert_rates = demog.get_fert(tot_per, fert_graph)[E:]
mort_rates0, infant_mort = demog.get_mort(tot_per, mort_graph)
# print (mort_rates)
mort_rates = mort_rates0[E:]
imm_rates = demog.get_imm_resid(tot_per, imm_graph)[E:]
omega_SS0, om_mat, omega_hat_path0, g_vec0, imm_path = demog.get_omega(
E, S, T, omega_graph)
omega_SS = omega_SS0[E:]
omega_hat_path = omega_hat_path0[E:, :]
g_vec = g_vec0[E:]
print(f'mort:{mort_rates.shape}, omega_SS:{omega_SS.shape}')
#HH parameters
beta_annual = .96
beta = beta_annual**(80 / S)
sigma = 2.2
nvec = np.ones(S)
cut = round(2 * S / 3)
nvec[cut:] = 0.2
chi_vec_bq = np.ones(S)
# Firm Parameters
alpha = 0.35
def get_reduced_parameters():
# Model Parameters
S = 10
J = 2
T = int(2 * S)
lambdas = np.array([.50, .50])
starting_age = 40
ending_age = 50
E = int(starting_age * (S / float(ending_age - starting_age)))
beta_annual = .96
beta = beta_annual**(float(ending_age - starting_age) / S)
sigma = 3.0
alpha = .35
Z = 1.0
delta_annual = .05
delta = 1 - ((1 - delta_annual)**(float(ending_age - starting_age) / S))
ltilde = 1.0
g_y_annual = 0.03
g_y = (1 + g_y_annual)**(float(ending_age - starting_age) / S) - 1
# Ellipse parameters
b_ellipse = 25.6594
k_ellipse = -26.4902
upsilon = 3.0542
# Tax parameters:
# Income Tax Parameters
mean_income_data = 84377.0
a_tax_income = 3.03452713268985e-06
b_tax_income = .222
c_tax_income = 133261.0
d_tax_income = .219
# Wealth tax params
# These are non-calibrated values, h and m just need
# need to be nonzero to avoid errors. When p_wealth
# is zero, there is no wealth tax.
h_wealth = 0.1
m_wealth = 1.0
p_wealth = 0.0
# Bequest and Payroll Taxes
tau_bq = np.zeros(J)
tau_payroll = 0.15
retire = np.round(9.0 * S / 16.0) - 1
# Simulation Parameters
MINIMIZER_TOL = 1e-3
MINIMIZER_OPTIONS = {'maxiter': 1}
PLOT_TPI = False
maxiter = 10
mindist_SS = 1e-3
mindist_TPI = 1e-6
nu = .4
flag_graphs = False
# Calibration parameters
# These guesses are close to the calibrated values
chi_b_guess = np.array([1, 100000])
chi_n_guess = np.array([5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
# Generate Income and Demographic parameters
omega, g_n_ss, omega_SS, surv_rate, rho, g_n_vector = get_omega(
S, T, starting_age, ending_age, E, flag_graphs)
e = np.array([[0.25, 1.25]] * 10)
allvars = dict(locals())
return allvars