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finished run_simulation.py documentation
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@evan-magnusson
evan-magnusson committed Jul 16, 2015
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188 changes: 106 additions & 82 deletions Python/CURRENT_DEBUGGED_VERSION/run_simulation.py
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Last updated 7/16/2015
This will run the steady state solver as well as time path iteration.
First this file generates demographic and ability variables, then it saves
the needed labor and wealth data moments, and then it saves all the
baseline parameters. After this, it runs the SS and TPI simulations.
If tax experiments are desired, the changed parameters are saved,
and then SS and TPI simulations are repeated. Finally, all .pyc files
are deleted.
This py-file calls the following other file(s):
income_polynomials.py
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'''
------------------------------------------------------------------------
Setting up the Model
Parameters
------------------------------------------------------------------------
S = number of periods an individual lives
J = number of different ability groups
T = number of time periods until steady state is reached
lambdas = desired percentiles of ability groups
starting_age = age of first members of cohort
ending age = age of the last members of cohort
E = number of cohorts before S=1
beta_annual = discount factor for one year
beta = discount factor for each age cohort
sigma = coefficient of relative risk aversion
alpha = capital share of income
Model Parameters:
------------------------------------------------------------------------
S = number of periods an individual lives (scalar)
J = number of different ability groups (scalar)
T = number of time periods until steady state is reached (scalar)
lambdas = percentiles for ability groups (Jx1 array)
starting_age = age of first members of cohort (scalar)
ending age = age of the last members of cohort (scalar)
E = number of cohorts before S=1 (scalar)
beta_annual = discount factor for one year (scalar)
beta = discount factor for each age cohort (scalar)
sigma = coefficient of relative risk aversion (scalar)
alpha = capital share of income (scalar)
Z = total factor productivity parameter in firms' production
function
delta_annual = depreciation rate of capital for one year
delta = depreciation rate of capital for each cohort
function (scalar)
delta_annual = depreciation rate of capital for one year (scalar)
delta = depreciation rate of capital for each cohort (scalar)
ltilde = measure of time each individual is endowed with each
period
g_y_annual = annual growth rate of technology
g_y = growth rate of technology for one cohort
maxiter = Maximum number of iterations that TPI will undergo
mindist_SS = Cut-off distance between iterations for SS
mindist_TPI = Cut-off distance between iterations for TPI
nu = contraction parameter in steady state iteration process
representing the weight on the new distribution gamma_nu
b_ellipse = value of b for elliptical fit of utility function
k_ellipse = value of k for elliptical fit of utility function
upsilon = value of omega for elliptical fit of utility function
period (scalar)
g_y_annual = annual growth rate of technology (scalar)
g_y = growth rate of technology for one cohort (scalar)
b_ellipse = value of b for elliptical fit of utility function (scalar)
k_ellipse = value of k for elliptical fit of utility function (scalar)
upsilon = value of omega for elliptical fit of utility function (scalar)
------------------------------------------------------------------------
Tax Parameters:
------------------------------------------------------------------------
mean_income_data = mean income from IRS data file used to calibrate income tax
(scalar)
a_tax_income = used to calibrate income tax (scalar)
b_tax_income = used to calibrate income tax (scalar)
c_tax_income = used to calibrate income tax (scalar)
d_tax_income = used to calibrate income tax (scalar)
retire = age in which individuals retire(scalar)
h_wealth = wealth tax parameter h
m_wealth = wealth tax parameter m
p_wealth = wealth tax parameter p
tau_bq = bequest tax (scalar)
h_wealth = wealth tax parameter h (scalar)
m_wealth = wealth tax parameter m (scalar)
p_wealth = wealth tax parameter p (scalar)
tau_bq = bequest tax (Jx1 array)
tau_payroll = payroll tax (scalar)
theta = payback value for payroll tax (scalar)
e = S x J matrix of age dependent possible working abilities
e_s
omega = T x S x J array of demographics
g_n_ss = 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
retire = age at which individuals retire (scalar)
------------------------------------------------------------------------
Simulation Parameters:
------------------------------------------------------------------------
maxiter = Maximum number of iterations that SS and TPI will undergo (scalar)
mindist_SS = Cut-off distance between iterations for SS (scalar)
mindist_TPI = Cut-off distance between iterations for TPI (scalar)
nu = contraction parameter in SS and TPI iteration process
representing the weight on the new distribution (scalar)
flag_graphs = Flag to prevent graphing from occuring in demographic, income,
wealth, and labor files (True=graph) (bool)
get_baseline = Flag to run baseline or tax experiments (bool)
calibrate_model = Flag to run calibration of chi values or not (bool)
chi_b_guess = Chi^b_j initial guess for model (Jx1 array)
(if no calibration occurs, these are the values that will be used for chi^b_j)
chi_n_guess = Chi^n_s initial guess for model (Sx1 array)
(if no calibration occurs, these are the values that will be used for chi^n_s)
------------------------------------------------------------------------
Demographics and Ability variables:
------------------------------------------------------------------------
omega = Time path of of population size for each age across T ((T+S)xS array)
g_n_ss = steady state population growth rate (scalar)
omega_SS = stationarized steady state population distribution (Sx1 array)
surv_rate = survival rates (Sx1 array)
rho = mortality rates (Sx1 array)
g_n_vector = population size for each T ((T+S)x1 array)
e = age dependent possible working abilities (SxJ array)
------------------------------------------------------------------------
'''

# Parameters
# Model Parameters
S = 80
J = 7
T = int(2 * S)
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ltilde = 1.0
g_y_annual = 0.03
g_y = (1 + g_y_annual)**(float(ending_age-starting_age)/S) - 1
# TPI parameters
maxiter = 250
mindist_SS = 1e-9
mindist_TPI = 1e-6
nu = .40
# Ellipse parameters
# 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
retire = np.round(9.0 * S / 16.0) - 1
# Wealth tax params
# These won't be used for the wealth tax, h and m just need
# need to be nonzero to avoid errors
# Wealth tax params
# These are non-calibrated values, h and m just need
# need to be nonzero to avoid errors
h_wealth = 0.1
m_wealth = 1.0
p_wealth = 0.0
# Tax parameters that are zeroed out for SS
# Initial taxes below
# Bequest and Payroll Taxes
tau_bq = np.zeros(J)
tau_payroll = 0.15
# Flag to prevent graphing from occuring in demographic, income, wealth, and labor files
flag_graphs = False
# Generate Income and Demographic parameters
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)
retire = np.round(9.0 * S / 16.0) - 1

# Calibration parameters
# Simulation Parameters
maxiter = 250
mindist_SS = 1e-9
mindist_TPI = 1e-6
nu = .40
flag_graphs = False
get_baseline = True
# Calibration parameters
calibrate_model = False
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
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, 28.90029708 , 29.83586775 , 30.87563699 , 31.91207845 , 33.07449767
, 34.27919965 , 35.57195873 , 36.95045988 , 38.62308152])

# Generate Wealth data moments
wealth_data.get_wealth_data(lambdas, J, flag_graphs)

# Generate labor data moments

labor_data.labor_data_moments(flag_graphs)

# 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)

get_baseline = True

# List of parameter names that will not be changing (unless we decide to
# change them for a tax experiment.
# 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',
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'''
------------------------------------------------------------------------
Run SS with minimization to fit chi_b and chi_n
Obtain wealth and labor distributions from data
------------------------------------------------------------------------
'''

# This is the simulation before getting the replacement rate values
# Generate Wealth data moments
wealth_data.get_wealth_data(lambdas, J, flag_graphs)
# Generate labor data moments
labor_data.labor_data_moments(flag_graphs)

'''
------------------------------------------------------------------------
Run baseline SS
------------------------------------------------------------------------
'''

dictionary = {}
for key in param_names:
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'''
------------------------------------------------------------------------
Run the baseline TPI simulation
Run baseline TPI
------------------------------------------------------------------------
'''

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------------------------------------------------------------------------
'''

# New Tax Parameters

# get_baseline = False
# d_tax_income = .42
get_baseline = False
# Altered parameters
d_tax_income = .42


# var_names = ['get_baseline', 'd_tax_income']
# dictionary = {}
# for key in var_names:
# dictionary[key] = globals()[key]
# pickle.dump(dictionary, open("OUTPUT/Saved_moments/params_changed.pkl", "w"))
# List of all parameters that have been changed for the tax experiment
var_names = ['get_baseline', 'd_tax_income']
dictionary = {}
for key in var_names:
dictionary[key] = globals()[key]
pickle.dump(dictionary, open("OUTPUT/Saved_moments/params_changed.pkl", "w"))

'''
------------------------------------------------------------------------
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