------------------------------------------------------------------------
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
, 4.57322063 , 4.53371545 , 4.29828515 , 4.10144524 , 3.8617942 , 3.57282
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
------------------------------------------------------------------------
'''
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
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
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
------------------------------------------------------------------------
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
, 4.57322063 , 4.53371545 , 4.29828515 , 4.10144524 , 3.8617942 , 3.57282
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_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
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
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
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
