def Euler1(w, r, e, n_guess, b1, b2, b3, BQ, factor, T_H, chi_b):
'''
Parameters:
w = wage rate (scalar)
r = rental rate (scalar)
e = distribution of abilities (SxJ array)
n_guess = distribution of labor (SxJ array)
b1 = distribution of capital in period t ((S-1) x J array)
b2 = distribution of capital in period t+1 ((S-1) x J array)
b3 = distribution of capital in period t+2 ((S-1) x J array)
B = distribution of incidental bequests (1 x J array)
factor = scaling value to make average income match data
T_H = lump sum transfer from the government to the households
xi = coefficient of relative risk aversion
chi_b = discount factor of savings
Returns:
Value of Euler error.
'''
BQ_euler = BQ.reshape(1, J)
tax1 = tax.total_taxes_SS(r, b1, w, e[:-1, :], n_guess[:-1, :], BQ_euler, lambdas, factor, T_H)
tax2 = tax.total_taxes_SS2(r, b2, w, e[1:, :], n_guess[1:, :], BQ_euler, lambdas, factor, T_H)
cons1 = get_cons(r, b1, w, e[:-1, :], n_guess[:-1, :], BQ_euler, lambdas, b2, g_y, tax1)
cons2 = get_cons(r, b2, w, e[1:, :], n_guess[1:, :], BQ_euler, lambdas, b3, g_y, tax2)
income = (r * b2 + w * e[1:, :] * n_guess[1:, :]) * factor
deriv = (
1 + r*(1-tax.tau_income(r, b1, w, e[1:, :], n_guess[1:, :], factor)-tax.tau_income_deriv(
r, b1, w, e[1:, :], n_guess[1:, :], factor)*income)-tax.tau_w_prime(b2)*b2-tax.tau_wealth(b2))
bequest_ut = rho[:-1].reshape(S-1, 1) * np.exp(-sigma * g_y) * chi_b[:-1].reshape(S-1, J) * b2 ** (-sigma)
euler = MUc(cons1) - beta * (1-rho[:-1].reshape(S-1, 1)) * deriv * MUc(
cons2) * np.exp(-sigma * g_y) - bequest_ut
return euler
def Euler1(w, r, e, L_guess, K1, K2, K3, B, factor, taulump, chi_b):
'''
Parameters:
w = wage rate (scalar)
r = rental rate (scalar)
e = distribution of abilities (SxJ array)
L_guess = distribution of labor (SxJ array)
K1 = distribution of capital in period t ((S-1) x J array)
K2 = distribution of capital in period t+1 ((S-1) x J array)
K3 = distribution of capital in period t+2 ((S-1) x J array)
B = distribution of incidental bequests (1 x J array)
factor = scaling value to make average income match data
taulump = lump sum transfer from the government to the households
xi = coefficient of relative risk aversion
chi_b = discount factor of savings
Returns:
Value of Euler error.
'''
B_euler = B.reshape(1, J)
tax1 = tax.total_taxes_SS(r, K1, w, e[:-1, :], L_guess[:-1, :], B_euler, bin_weights, factor, taulump)
tax2 = tax.total_taxes_SS2(r, K2, w, e[1:, :], L_guess[1:, :], B_euler, bin_weights, factor, taulump)
cons1 = get_cons(r, K1, w, e[:-1, :], L_guess[:-1, :], B_euler, bin_weights, K2, g_y, tax1)
cons2 = get_cons(r, K2, w, e[1:, :], L_guess[1:, :], B_euler, bin_weights, K3, g_y, tax2)
income = (r * K2 + w * e[1:, :] * L_guess[1:, :]) * factor
deriv = (
1 + r*(1-tax.tau_income(r, K1, w, e[1:, :], L_guess[1:, :], factor)-tax.tau_income_deriv(
r, K1, w, e[1:, :], L_guess[1:, :], factor)*income)-tax.tau_w_prime(K2)*K2-tax.tau_wealth(K2))
bequest_ut = (1-surv_rate[:-1].reshape(S-1, 1)) * np.exp(-sigma * g_y) * chi_b[:-1].reshape(S-1, J) * K2 ** (-sigma)
euler = MUc(cons1) - beta * surv_rate[:-1].reshape(S-1, 1) * deriv * MUc(
cons2) * np.exp(-sigma * g_y) - bequest_ut
return euler
def euler_savings_func(w, r, e, n_guess, b_s, b_splus1, b_splus2, BQ, factor,
T_H, chi_b, params, theta, tau_bq, rho, lambdas):
'''
This function is usually looped through over J, so it does one ability group at a time.
Inputs:
w = wage rate (scalar)
r = rental rate (scalar)
e = ability levels (Sx1 array)
n_guess = labor distribution (Sx1 array)
b_s = wealth holdings at the start of a period (Sx1 array)
b_splus1 = wealth holdings for the next period (Sx1 array)
b_splus2 = wealth holdings for 2 periods ahead (Sx1 array)
BQ = aggregate bequests for a certain ability (scalar)
factor = scaling factor to convert to dollars (scalar)
T_H = lump sum tax (scalar)
chi_b = chi^b_j for a certain ability (scalar)
params = parameter list (list)
theta = replacement rate for a certain ability (scalar)
tau_bq = bequest tax rate (scalar)
rho = mortality rate (Sx1 array)
lambdas = ability weight (scalar)
Output:
euler = Value of savings euler error (Sx1 array)
'''
J, S, T, beta, sigma, alpha, Z, delta, ltilde, nu, g_y, g_n_ss, tau_payroll, retire, mean_income_data, a_tax_income, b_tax_income, c_tax_income, d_tax_income, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = params
# In order to not have 2 savings euler equations (one that solves the first S-1 equations, and one that solves the last one),
# we combine them. In order to do this, we have to compute a consumption term in period t+1, which requires us to have a shifted
# e and n matrix. We append a zero on the end of both of these so they will be the right size. We could append any value to them,
# since in the euler equation, the coefficient on the marginal utility of consumption for this term will be zero (since rho is one).
e_extended = np.array(list(e) + [0])
n_extended = np.array(list(n_guess) + [0])
tax1 = tax.total_taxes(r, b_s, w, e, n_guess, BQ, lambdas, factor, T_H,
None, 'SS', False, params, theta, tau_bq)
tax2 = tax.total_taxes(r, b_splus1, w, e_extended[1:], n_extended[1:], BQ,
lambdas, factor, T_H, None, 'SS', True, params,
theta, tau_bq)
cons1 = get_cons(r, b_s, w, e, n_guess, BQ, lambdas, b_splus1, params,
tax1)
cons2 = get_cons(r, b_splus1, w, e_extended[1:], n_extended[1:], BQ,
lambdas, b_splus2, params, tax2)
income = (r * b_splus1 + w * e_extended[1:] * n_extended[1:]) * factor
deriv = (1 + r *
(1 - tax.tau_income(r, b_splus1, w, e_extended[1:],
n_extended[1:], factor, params) -
tax.tau_income_deriv(r, b_splus1, w, e_extended[1:],
n_extended[1:], factor, params) * income) -
tax.tau_w_prime(b_splus1, params) * b_splus1 -
tax.tau_wealth(b_splus1, params))
savings_ut = rho * np.exp(-sigma * g_y) * chi_b * b_splus1**(-sigma)
# Again, not who in this equation, the (1-rho) term will zero out in the last period, so the last entry of cons2 can be complete
# gibberish (which it is). It just has to exist so cons2 is the right size to match all other arrays in the equation.
euler = marg_ut_cons(cons1,
params) - beta * (1 - rho) * deriv * marg_ut_cons(
cons2, params) * np.exp(-sigma * g_y) - savings_ut
return euler
def euler_savings_func(w, r, e, n_guess, b_s, b_splus1, b_splus2, BQ, factor, T_H, chi_b, params, theta, tau_bq, rho, lambdas):
'''
This function is usually looped through over J, so it does one ability group at a time.
Inputs:
w = wage rate (scalar)
r = rental rate (scalar)
e = ability levels (Sx1 array)
n_guess = labor distribution (Sx1 array)
b_s = wealth holdings at the start of a period (Sx1 array)
b_splus1 = wealth holdings for the next period (Sx1 array)
b_splus2 = wealth holdings for 2 periods ahead (Sx1 array)
BQ = aggregate bequests for a certain ability (scalar)
factor = scaling factor to convert to dollars (scalar)
T_H = lump sum tax (scalar)
chi_b = chi^b_j for a certain ability (scalar)
params = parameter list (list)
theta = replacement rate for a certain ability (scalar)
tau_bq = bequest tax rate (scalar)
rho = mortality rate (Sx1 array)
lambdas = ability weight (scalar)
Output:
euler = Value of savings euler error (Sx1 array)
'''
J, S, T, beta, sigma, alpha, Z, delta, ltilde, nu, g_y, g_n_ss, tau_payroll, retire, mean_income_data, a_tax_income, b_tax_income, c_tax_income, d_tax_income, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = params
# In order to not have 2 savings euler equations (one that solves the first S-1 equations, and one that solves the last one),
# we combine them. In order to do this, we have to compute a consumption term in period t+1, which requires us to have a shifted
# e and n matrix. We append a zero on the end of both of these so they will be the right size. We could append any value to them,
# since in the euler equation, the coefficient on the marginal utility of consumption for this term will be zero (since rho is one).
e_extended = np.array(list(e) + [0])
n_extended = np.array(list(n_guess) + [0])
tax1 = tax.total_taxes(r, b_s, w, e, n_guess, BQ, lambdas, factor, T_H, None, 'SS', False, params, theta, tau_bq)
tax2 = tax.total_taxes(r, b_splus1, w, e_extended[1:], n_extended[1:], BQ, lambdas, factor, T_H, None, 'SS', True, params, theta, tau_bq)
cons1 = get_cons(r, b_s, w, e, n_guess, BQ, lambdas, b_splus1, params, tax1)
cons2 = get_cons(r, b_splus1, w, e_extended[1:], n_extended[1:], BQ, lambdas, b_splus2, params, tax2)
income = (r * b_splus1 + w * e_extended[1:] * n_extended[1:]) * factor
deriv = (
1 + r*(1-tax.tau_income(r, b_splus1, w, e_extended[1:], n_extended[1:], factor, params)-tax.tau_income_deriv(
r, b_splus1, w, e_extended[1:], n_extended[1:], factor, params)*income)-tax.tau_w_prime(b_splus1, params)*b_splus1-tax.tau_wealth(b_splus1, params))
savings_ut = rho * np.exp(-sigma * g_y) * chi_b * b_splus1 ** (-sigma)
# Again, not who in this equation, the (1-rho) term will zero out in the last period, so the last entry of cons2 can be complete
# gibberish (which it is). It just has to exist so cons2 is the right size to match all other arrays in the equation.
euler = marg_ut_cons(cons1, params) - beta * (1-rho) * deriv * marg_ut_cons(
cons2, params) * np.exp(-sigma * g_y) - savings_ut
return euler
def euler_savings_func(w, r, e, n_guess, b_s, b_splus1, b_splus2, BQ, factor, T_H, chi_b, params, theta, tau_bq, rho, lambdas):
'''
Inputs:
w = wage rate (scalar)
r = rental rate (scalar)
e = distribution of abilities (SxJ array)
n_guess = distribution of labor (SxJ array)
b_s = distribution of capital in period t (S x J array)
b_splus1 = distribution of capital in period t+1 (S x J array)
b_splus2 = distribution of capital in period t+2 (S x J array)
BQ = distribution of incidental bequests (1 x J array)
factor = scaling value to make average income match data
T_H = lump sum transfer from the government to the households
chi_b = discount factor of savings
params = parameters
theta = replacement rates
tau_bq = bequest tax parameters
rho = mortality rates
lambdas = bin weights
Returns:
Value of Euler error.
'''
J, S, T, beta, sigma, alpha, Z, delta, ltilde, nu, g_y, tau_payroll, retire, mean_income_data, a_tax_income, b_tax_income, c_tax_income, d_tax_income, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = params
e_extended = np.array(list(e) + [0])
n_extended = np.array(list(n_guess) + [0])
tax1 = tax.total_taxes(r, b_s, w, e, n_guess, BQ, lambdas, factor, T_H, None, 'SS', False, params, theta, tau_bq)
tax2 = tax.total_taxes(r, b_splus1, w, e_extended[1:], n_extended[1:], BQ, lambdas, factor, T_H, None, 'SS', True, params, theta, tau_bq)
cons1 = get_cons(r, b_s, w, e, n_guess, BQ, lambdas, b_splus1, params, tax1)
cons2 = get_cons(r, b_splus1, w, e_extended[1:], n_extended[1:], BQ, lambdas, b_splus2, params, tax2)
income = (r * b_splus1 + w * e_extended[1:] * n_extended[1:]) * factor
deriv = (
1 + r*(1-tax.tau_income(r, b_s, w, e_extended[1:], n_extended[1:], factor, params)-tax.tau_income_deriv(
r, b_s, w, e_extended[1:], n_extended[1:], factor, params)*income)-tax.tau_w_prime(b_splus1, params)*b_splus1-tax.tau_wealth(b_splus1, params))
savings_ut = rho * np.exp(-sigma * g_y) * chi_b * b_splus1 ** (-sigma)
euler = marg_ut_cons(cons1, params) - beta * (1-rho) * deriv * marg_ut_cons(
cons2, params) * np.exp(-sigma * g_y) - savings_ut
return euler
def Steady_state_TPI_solver(guesses, winit, rinit, BQinit, T_H_init, factor, j, s, t, params, theta, tau_bq, rho, lambdas, e, initial_b, chi_b, chi_n):
'''
Parameters:
guesses = distribution of capital and labor in period t
((S-1)*S*J x 1 list)
winit = wage rate (scalar)
rinit = rental rate (scalar)
t = time period
Returns:
Value of Euler error. (as an 2*S*J x 1 list)
'''
J, S, T, beta, sigma, alpha, Z, delta, ltilde, nu, g_y, tau_payroll, retire, mean_income_data, a_tax_income, b_tax_income, c_tax_income, d_tax_income, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = params
length = len(guesses)/2
b_guess = np.array(guesses[:length])
n_guess = np.array(guesses[length:])
if length == S:
b_s = np.array([0] + list(b_guess[:-1]))
else:
b_s = np.array([(initial_b[-(s+3), j])] + list(b_guess[:-1]))
b_splus1 = b_guess
b_splus2 = np.array(list(b_guess[1:]) + [0])
w_s = winit[t:t+length]
w_splus1 = winit[t+1:t+length+1]
r_s = rinit[t:t+length]
r_splus1 = rinit[t+1:t+length+1]
n_s = n_guess
n_extended = np.array(list(n_guess[1:]) + [0])
e_s = e[-length:, j]
e_extended = np.array(list(e[-length+1:, j]) + [0])
BQ_s = BQinit[t:t+length]
BQ_splus1 = BQinit[t+1:t+length+1]
T_H_s = T_H_init[t:t+length]
T_H_splus1 = T_H_init[t+1:t+length+1]
# Savings euler equations
tax_s = tax.total_taxes(r_s, b_s, w_s, e_s, n_s, BQ_s, lambdas[j], factor, T_H_s, j, 'TPI', False, params, theta, tau_bq)
tax_splus1 = tax.total_taxes(r_splus1, b_splus1, w_splus1, e_extended, n_extended, BQ_splus1, lambdas[j], factor, T_H_splus1, j, 'TPI', True, params, theta, tau_bq)
cons_s = house.get_cons(r_s, b_s, w_s, e_s, n_s, BQ_s, lambdas[j], b_splus1, params, tax_s)
cons_splus1 = house.get_cons(r_splus1, b_splus1, w_splus1, e_extended, n_extended, BQ_splus1, lambdas[j], b_splus2, params, tax_splus1)
income_splus1 = (r_splus1 * b_splus1 + w_splus1 * e_extended * n_extended) * factor
savings_ut = rho[-(length):] * np.exp(-sigma * g_y) * chi_b[-(length):, j] * b_splus1 ** (-sigma)
deriv_savings = 1 + r_splus1 * (1 - tax.tau_income(
r_splus1, b_splus1, w_splus1, e_extended, n_extended, factor, params) - tax.tau_income_deriv(
r_splus1, b_splus1, w_splus1, e_extended, n_extended, factor, params) * income_splus1) - tax.tau_w_prime(
b_splus1, params)*b_splus1 - tax.tau_wealth(b_splus1, params)
error1 = house.marg_ut_cons(cons_s, params) - beta * (1-rho[-(length):]) * np.exp(-sigma * g_y) * deriv_savings * house.marg_ut_cons(
cons_splus1, params) - savings_ut
# Labor leisure euler equations
income_s = (r_s * b_s + w_s * e_s * n_s) * factor
deriv_laborleisure = 1 - tau_payroll - tax.tau_income(r_s, b_s, w_s, e_s, n_s, factor, params) - tax.tau_income_deriv(
r_s, b_s, w_s, e_s, n_s, factor, params) * income_s
error2 = house.marg_ut_cons(cons_s, params) * w_s * e[-(length):, j] * deriv_laborleisure - house.marg_ut_labor(n_s, chi_n[-length:], params)
# Check and punish constraint violations
mask1 = n_guess < 0
error2[mask1] += 1e12
mask2 = n_guess > ltilde
error2[mask2] += 1e12
mask3 = cons_s < 0
error2[mask3] += 1e12
mask4 = b_guess <= 0
error2[mask4] += 1e12
mask5 = cons_splus1 < 0
error2[mask5] += 1e12
return list(error1.flatten()) + list(error2.flatten())
def Steady_state_TPI_solver(guesses, winit, rinit, BQinit, T_H_init, factor, j, s, t, params, theta, tau_bq, rho, lambdas, e, initial_b, chi_b, chi_n):
'''
Parameters:
guesses = distribution of capital and labor (various length list)
winit = wage rate ((T+S)x1 array)
rinit = rental rate ((T+S)x1 array)
BQinit = aggregate bequests ((T+S)x1 array)
T_H_init = lump sum tax over time ((T+S)x1 array)
factor = scaling factor (scalar)
j = which ability type is being solved for (scalar)
s = which upper triangle loop is being solved for (scalar)
t = which diagonal is being solved for (scalar)
params = list of parameters (list)
theta = replacement rates (Jx1 array)
tau_bq = bequest tax rate (Jx1 array)
rho = mortalit rate (Sx1 array)
lambdas = ability weights (Jx1 array)
e = ability type (SxJ array)
initial_b = capital stock distribution in period 0 (SxJ array)
chi_b = chi^b_j (Jx1 array)
chi_n = chi^n_s (Sx1 array)
Output:
Value of Euler error (various length list)
'''
J, S, T, beta, sigma, alpha, Z, delta, ltilde, nu, g_y, g_n_ss, tau_payroll, retire, mean_income_data, a_tax_income, b_tax_income, c_tax_income, d_tax_income, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = params
length = len(guesses)/2
b_guess = np.array(guesses[:length])
n_guess = np.array(guesses[length:])
if length == S:
b_s = np.array([0] + list(b_guess[:-1]))
else:
b_s = np.array([(initial_b[-(s+3), j])] + list(b_guess[:-1]))
b_splus1 = b_guess
b_splus2 = np.array(list(b_guess[1:]) + [0])
w_s = winit[t:t+length]
w_splus1 = winit[t+1:t+length+1]
r_s = rinit[t:t+length]
r_splus1 = rinit[t+1:t+length+1]
n_s = n_guess
n_extended = np.array(list(n_guess[1:]) + [0])
e_s = e[-length:, j]
e_extended = np.array(list(e[-length+1:, j]) + [0])
BQ_s = BQinit[t:t+length]
BQ_splus1 = BQinit[t+1:t+length+1]
T_H_s = T_H_init[t:t+length]
T_H_splus1 = T_H_init[t+1:t+length+1]
# Savings euler equations
tax_s = tax.total_taxes(r_s, b_s, w_s, e_s, n_s, BQ_s, lambdas[j], factor, T_H_s, j, 'TPI', False, params, theta, tau_bq)
tax_splus1 = tax.total_taxes(r_splus1, b_splus1, w_splus1, e_extended, n_extended, BQ_splus1, lambdas[j], factor, T_H_splus1, j, 'TPI', True, params, theta, tau_bq)
cons_s = house.get_cons(r_s, b_s, w_s, e_s, n_s, BQ_s, lambdas[j], b_splus1, params, tax_s)
cons_splus1 = house.get_cons(r_splus1, b_splus1, w_splus1, e_extended, n_extended, BQ_splus1, lambdas[j], b_splus2, params, tax_splus1)
income_splus1 = (r_splus1 * b_splus1 + w_splus1 * e_extended * n_extended) * factor
savings_ut = rho[-(length):] * np.exp(-sigma * g_y) * chi_b[-(length):, j] * b_splus1 ** (-sigma)
deriv_savings = 1 + r_splus1 * (1 - tax.tau_income(
r_splus1, b_splus1, w_splus1, e_extended, n_extended, factor, params) - tax.tau_income_deriv(
r_splus1, b_splus1, w_splus1, e_extended, n_extended, factor, params) * income_splus1) - tax.tau_w_prime(
b_splus1, params)*b_splus1 - tax.tau_wealth(b_splus1, params)
error1 = house.marg_ut_cons(cons_s, params) - beta * (1-rho[-(length):]) * np.exp(-sigma * g_y) * deriv_savings * house.marg_ut_cons(
cons_splus1, params) - savings_ut
# Labor leisure euler equations
income_s = (r_s * b_s + w_s * e_s * n_s) * factor
deriv_laborleisure = 1 - tau_payroll - tax.tau_income(r_s, b_s, w_s, e_s, n_s, factor, params) - tax.tau_income_deriv(
r_s, b_s, w_s, e_s, n_s, factor, params) * income_s
error2 = house.marg_ut_cons(cons_s, params) * w_s * e[-(length):, j] * deriv_laborleisure - house.marg_ut_labor(n_s, chi_n[-length:], params)
# Check and punish constraint violations
mask1 = n_guess < 0
error2[mask1] += 1e12
mask2 = n_guess > ltilde
error2[mask2] += 1e12
mask3 = cons_s < 0
error2[mask3] += 1e12
mask4 = b_guess <= 0
error2[mask4] += 1e12
mask5 = cons_splus1 < 0
error2[mask5] += 1e12
return list(error1.flatten()) + list(error2.flatten())
def Euler_Error(guesses, winit, rinit, Binit, Tinit, t):
'''
Parameters:
guesses = distribution of capital and labor in period t
((S-1)*S*J x 1 list)
winit = wage rate (scalar)
rinit = rental rate (scalar)
t = time period
Returns:
Value of Euler error. (as an 2*S*J x 1 list)
'''
length = len(guesses) / 2
K_guess = np.array(guesses[:length])
L_guess = np.array(guesses[length:])
if length == S:
K1 = np.array([0] + list(K_guess[:-2]))
else:
K1 = np.array([(initial_K[-(s + 2), j])] + list(K_guess[:-2]))
K2 = K_guess[:-1]
K3 = K_guess[1:]
w1 = winit[t:t + length - 1]
w2 = winit[t + 1:t + length]
r1 = rinit[t:t + length - 1]
r2 = rinit[t + 1:t + length]
l1 = L_guess[:-1]
l2 = L_guess[1:]
e1 = e[-length:-1, j]
e2 = e[-length + 1:, j]
B1 = Binit[t:t + length - 1]
B2 = Binit[t + 1:t + length]
T1 = Tinit[t:t + length - 1]
T2 = Tinit[t + 1:t + length]
# Euler 1 equations
tax11 = tax.total_taxes_TPI1(r1, K1, w1, e1, l1, B1, bin_weights[j],
factor_ss, T1, j)
tax12 = tax.total_taxes_TPI1_2(r2, K2, w2, e2, l2, B2, bin_weights[j],
factor_ss, T2, j)
cons11 = get_cons(r1, K1, w1, e1, l1, B1, bin_weights[j], K2, g_y, tax11)
cons12 = get_cons(r2, K2, w2, e2, l2, B2, bin_weights[j], K3, g_y, tax12)
wealth1 = (r2 * K2 + w2 * e2 * l2) * factor_ss
bequest_ut = (1 - surv_rate[-(length):-1]) * np.exp(
-sigma * g_y) * chi_b[-(length):-1, j] * K2**(-sigma)
deriv1 = 1 + r2 * (1 - tax.tau_income(r2, K2, w2, e2, l2, factor_ss) -
tax.tau_income_deriv(r2, K2, w2, e2, l2, factor_ss) *
wealth1) - tax.tau_w_prime(K2) * K2 - tax.tau_wealth(K2)
error1 = MUc(cons11) - beta * surv_rate[-(length):-1] * np.exp(
-sigma * g_y) * deriv1 * MUc(cons12) - bequest_ut
# Euler 2 equations
if length == S:
K1_2 = np.array([0] + list(K_guess[:-1]))
else:
K1_2 = np.array([(initial_K[-(s + 2), j])] + list(K_guess[:-1]))
K2_2 = K_guess
w = winit[t:t + length]
r = rinit[t:t + length]
B = Binit[t:t + length]
Tl = Tinit[t:t + length]
tax2 = tax.total_taxes_TPI2(r, K1_2, w, e[-(length):, j], L_guess, B,
bin_weights[j], factor_ss, Tl, j)
cons2 = get_cons(r, K1_2, w, e[-(length):, j], L_guess, B, bin_weights[j],
K2_2, g_y, tax2)
wealth2 = (r * K1_2 + w * e[-(length):, j] * L_guess) * factor_ss
deriv2 = 1 - tau_payroll - tax.tau_income(
r, K1_2, w,
e[-(length):, j], L_guess, factor_ss) - tax.tau_income_deriv(
r, K1_2, w, e[-(length):, j], L_guess, factor_ss) * wealth2
error2 = MUc(cons2) * w * e[-(length):, j] * deriv2 - MUl2(
L_guess, chi_n[-length:])
# Euler 3 equations
tax3 = tax.total_taxes_eul3_TPI(r[-1], K_guess[-2], w[-1], e[-1, j],
L_guess[-1], B[-1], bin_weights[j],
factor_ss, Tl[-1], j)
cons3 = get_cons(r[-1], K_guess[-2], w[-1], e[-1, j], L_guess[-1], B[-1],
bin_weights[j], K_guess[-1], g_y, tax3)
error3 = MUc(cons3) - np.exp(-sigma * g_y) * MUb2(K_guess[-1], chi_b[:, j])
# Check and punish constraint violations
mask1 = L_guess < 0
error2[mask1] += 1e12
mask2 = L_guess > ltilde
error2[mask2] += 1e12
mask3 = cons2 < 0
error2[mask3] += 1e12
mask4 = K_guess <= 0
error2[mask4] += 1e12
return list(error1.flatten()) + list(error2.flatten()) + list(
error3.flatten())
def Euler_Error(guesses, winit, rinit, Binit, Tinit, t):
"""
Parameters:
guesses = distribution of capital and labor in period t
((S-1)*S*J x 1 list)
winit = wage rate (scalar)
rinit = rental rate (scalar)
t = time period
Returns:
Value of Euler error. (as an 2*S*J x 1 list)
"""
length = len(guesses) / 2
K_guess = np.array(guesses[:length])
L_guess = np.array(guesses[length:])
if length == S:
K1 = np.array([0] + list(K_guess[:-2]))
else:
K1 = np.array([(initial_K[-(s + 2), j])] + list(K_guess[:-2]))
K2 = K_guess[:-1]
K3 = K_guess[1:]
w1 = winit[t : t + length - 1]
w2 = winit[t + 1 : t + length]
r1 = rinit[t : t + length - 1]
r2 = rinit[t + 1 : t + length]
l1 = L_guess[:-1]
l2 = L_guess[1:]
e1 = e[-length:-1, j]
e2 = e[-length + 1 :, j]
B1 = Binit[t : t + length - 1]
B2 = Binit[t + 1 : t + length]
T1 = Tinit[t : t + length - 1]
T2 = Tinit[t + 1 : t + length]
# Euler 1 equations
tax11 = tax.total_taxes_TPI1(r1, K1, w1, e1, l1, B1, bin_weights[j], factor_ss, T1, j)
tax12 = tax.total_taxes_TPI1_2(r2, K2, w2, e2, l2, B2, bin_weights[j], factor_ss, T2, j)
cons11 = get_cons(r1, K1, w1, e1, l1, B1, bin_weights[j], K2, g_y, tax11)
cons12 = get_cons(r2, K2, w2, e2, l2, B2, bin_weights[j], K3, g_y, tax12)
wealth1 = (r2 * K2 + w2 * e2 * l2) * factor_ss
bequest_ut = (1 - surv_rate[-(length):-1]) * np.exp(-sigma * g_y) * chi_b[-(length):-1, j] * K2 ** (-sigma)
deriv1 = (
1
+ r2
* (
1
- tax.tau_income(r2, K2, w2, e2, l2, factor_ss)
- tax.tau_income_deriv(r2, K2, w2, e2, l2, factor_ss) * wealth1
)
- tax.tau_w_prime(K2) * K2
- tax.tau_wealth(K2)
)
error1 = MUc(cons11) - beta * surv_rate[-(length):-1] * np.exp(-sigma * g_y) * deriv1 * MUc(cons12) - bequest_ut
# Euler 2 equations
if length == S:
K1_2 = np.array([0] + list(K_guess[:-1]))
else:
K1_2 = np.array([(initial_K[-(s + 2), j])] + list(K_guess[:-1]))
K2_2 = K_guess
w = winit[t : t + length]
r = rinit[t : t + length]
B = Binit[t : t + length]
Tl = Tinit[t : t + length]
tax2 = tax.total_taxes_TPI2(r, K1_2, w, e[-(length):, j], L_guess, B, bin_weights[j], factor_ss, Tl, j)
cons2 = get_cons(r, K1_2, w, e[-(length):, j], L_guess, B, bin_weights[j], K2_2, g_y, tax2)
wealth2 = (r * K1_2 + w * e[-(length):, j] * L_guess) * factor_ss
deriv2 = (
1
- tau_payroll
- tax.tau_income(r, K1_2, w, e[-(length):, j], L_guess, factor_ss)
- tax.tau_income_deriv(r, K1_2, w, e[-(length):, j], L_guess, factor_ss) * wealth2
)
error2 = MUc(cons2) * w * e[-(length):, j] * deriv2 - MUl2(L_guess, chi_n[-length:])
# Euler 3 equations
tax3 = tax.total_taxes_eul3_TPI(
r[-1], K_guess[-2], w[-1], e[-1, j], L_guess[-1], B[-1], bin_weights[j], factor_ss, Tl[-1], j
)
cons3 = get_cons(r[-1], K_guess[-2], w[-1], e[-1, j], L_guess[-1], B[-1], bin_weights[j], K_guess[-1], g_y, tax3)
error3 = MUc(cons3) - np.exp(-sigma * g_y) * MUb2(K_guess[-1], chi_b[:, j])
# Check and punish constraint violations
mask1 = L_guess < 0
error2[mask1] += 1e12
mask2 = L_guess > ltilde
error2[mask2] += 1e12
mask3 = cons2 < 0
error2[mask3] += 1e12
mask4 = K_guess <= 0
error2[mask4] += 1e12
return list(error1.flatten()) + list(error2.flatten()) + list(error3.flatten())
def Steady_state_TPI_solver(guesses, winit, rinit, BQinit, T_H_init, factor, j,
s, t, params, theta, tau_bq, rho, lambdas, e,
initial_b, chi_b, chi_n):
'''
Parameters:
guesses = distribution of capital and labor (various length list)
winit = wage rate ((T+S)x1 array)
rinit = rental rate ((T+S)x1 array)
BQinit = aggregate bequests ((T+S)x1 array)
T_H_init = lump sum tax over time ((T+S)x1 array)
factor = scaling factor (scalar)
j = which ability type is being solved for (scalar)
s = which upper triangle loop is being solved for (scalar)
t = which diagonal is being solved for (scalar)
params = list of parameters (list)
theta = replacement rates (Jx1 array)
tau_bq = bequest tax rate (Jx1 array)
rho = mortalit rate (Sx1 array)
lambdas = ability weights (Jx1 array)
e = ability type (SxJ array)
initial_b = capital stock distribution in period 0 (SxJ array)
chi_b = chi^b_j (Jx1 array)
chi_n = chi^n_s (Sx1 array)
Output:
Value of Euler error (various length list)
'''
J, S, T, beta, sigma, alpha, Z, delta, ltilde, nu, g_y, g_n_ss, tau_payroll, retire, mean_income_data, a_tax_income, b_tax_income, c_tax_income, d_tax_income, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = params
length = len(guesses) / 2
b_guess = np.array(guesses[:length])
n_guess = np.array(guesses[length:])
if length == S:
b_s = np.array([0] + list(b_guess[:-1]))
else:
b_s = np.array([(initial_b[-(s + 3), j])] + list(b_guess[:-1]))
b_splus1 = b_guess
b_splus2 = np.array(list(b_guess[1:]) + [0])
w_s = winit[t:t + length]
w_splus1 = winit[t + 1:t + length + 1]
r_s = rinit[t:t + length]
r_splus1 = rinit[t + 1:t + length + 1]
n_s = n_guess
n_extended = np.array(list(n_guess[1:]) + [0])
e_s = e[-length:, j]
e_extended = np.array(list(e[-length + 1:, j]) + [0])
BQ_s = BQinit[t:t + length]
BQ_splus1 = BQinit[t + 1:t + length + 1]
T_H_s = T_H_init[t:t + length]
T_H_splus1 = T_H_init[t + 1:t + length + 1]
# Savings euler equations
tax_s = tax.total_taxes(r_s, b_s, w_s, e_s, n_s, BQ_s, lambdas[j], factor,
T_H_s, j, 'TPI', False, params, theta, tau_bq)
tax_splus1 = tax.total_taxes(r_splus1, b_splus1, w_splus1, e_extended,
n_extended, BQ_splus1, lambdas[j], factor,
T_H_splus1, j, 'TPI', True, params, theta,
tau_bq)
cons_s = house.get_cons(r_s, b_s, w_s, e_s, n_s, BQ_s, lambdas[j],
b_splus1, params, tax_s)
cons_splus1 = house.get_cons(r_splus1, b_splus1, w_splus1, e_extended,
n_extended, BQ_splus1, lambdas[j], b_splus2,
params, tax_splus1)
income_splus1 = (r_splus1 * b_splus1 +
w_splus1 * e_extended * n_extended) * factor
savings_ut = rho[-(length):] * np.exp(
-sigma * g_y) * chi_b[-(length):, j] * b_splus1**(-sigma)
deriv_savings = 1 + r_splus1 * (
1 - tax.tau_income(r_splus1, b_splus1, w_splus1, e_extended,
n_extended, factor, params) -
tax.tau_income_deriv(r_splus1, b_splus1, w_splus1, e_extended,
n_extended, factor, params) * income_splus1
) - tax.tau_w_prime(b_splus1, params) * b_splus1 - tax.tau_wealth(
b_splus1, params)
error1 = house.marg_ut_cons(
cons_s, params) - beta * (1 - rho[-(length):]) * np.exp(
-sigma * g_y) * deriv_savings * house.marg_ut_cons(
cons_splus1, params) - savings_ut
# Labor leisure euler equations
income_s = (r_s * b_s + w_s * e_s * n_s) * factor
deriv_laborleisure = 1 - tau_payroll - tax.tau_income(
r_s, b_s, w_s, e_s, n_s, factor, params) - tax.tau_income_deriv(
r_s, b_s, w_s, e_s, n_s, factor, params) * income_s
error2 = house.marg_ut_cons(cons_s, params) * w_s * e[
-(length):, j] * deriv_laborleisure - house.marg_ut_labor(
n_s, chi_n[-length:], params)
# Check and punish constraint violations
mask1 = n_guess < 0
error2[mask1] += 1e12
mask2 = n_guess > ltilde
error2[mask2] += 1e12
mask3 = cons_s < 0
error2[mask3] += 1e12
mask4 = b_guess <= 0
error2[mask4] += 1e12
mask5 = cons_splus1 < 0
error2[mask5] += 1e12
return list(error1.flatten()) + list(error2.flatten())