def test_FOC_savings(model_vars, params, expected):
# Test FOC condition for household's choice of savings
(r, w, b, b_splus1, n, BQ, factor, tr, theta, tau_c, etr_params,
mtry_params, t, j, method) = model_vars
if j is not None:
test_value = household.FOC_savings(r, w, b, b_splus1, n, BQ, factor,
tr, theta, params.e[:, j],
params.rho, tau_c, etr_params,
mtry_params, t, j, params, method)
else:
test_value = household.FOC_savings(r, w, b, b_splus1, n, BQ,
factor, tr, theta,
np.squeeze(params.e), params.rho,
tau_c, etr_params, mtry_params, t,
j, params, method)
assert np.allclose(test_value, expected)
def euler_equation_solver(guesses, *args):
'''
Finds the euler errors for certain b and n, one ability type at a
time.
Args:
guesses (Numpy array): initial guesses for b and n, lenth 2S
args (tuple): tuple of arguments (r, w, bq, TR, factor, j, p)
w (scalar): real wage rate
bq (Numpy array): bequest amounts by age, length S
tr (scalar): government transfer amount by age, length S
factor (scalar): scaling factor converting model units to dollars
p (OG-USA Specifications object): model parameters
Returns:
errros (Numpy array): errors from FOCs, length 2S
'''
(r, w, bq, tr, factor, j, p) = args
b_guess = np.array(guesses[:p.S])
n_guess = np.array(guesses[p.S:])
b_s = np.array([0] + list(b_guess[:-1]))
b_splus1 = b_guess
theta = tax.replacement_rate_vals(n_guess, w, factor, j, p)
error1 = household.FOC_savings(r, w, b_s, b_splus1, n_guess, bq, factor,
tr, theta, p.e[:, j], p.rho,
p.tau_c[-1, :, j], p.etr_params[-1, :, :],
p.mtry_params[-1, :, :], None, j, p, 'SS')
error2 = household.FOC_labor(r, w, b_s, b_splus1, n_guess, bq, factor, tr,
theta, p.chi_n, p.e[:, j], p.tau_c[-1, :, j],
p.etr_params[-1, :, :],
p.mtrx_params[-1, :, :], None, j, p, 'SS')
# Put in constraints for consumption and savings.
# According to the euler equations, they can be negative. When
# Chi_b is large, they will be. This prevents that from happening.
# I'm not sure if the constraints are needed for labor.
# But we might as well put them in for now.
mask1 = n_guess < 0
mask2 = n_guess > p.ltilde
mask3 = b_guess <= 0
mask4 = np.isnan(n_guess)
mask5 = np.isnan(b_guess)
error2[mask1] = 1e14
error2[mask2] = 1e14
error1[mask3] = 1e14
error1[mask5] = 1e14
error2[mask4] = 1e14
taxes = tax.total_taxes(r, w, b_s, n_guess, bq, factor, tr, theta, None, j,
False, 'SS', p.e[:, j], p.etr_params[-1, :, :], p)
cons = household.get_cons(r, w, b_s, b_splus1, n_guess, bq, taxes,
p.e[:, j], p.tau_c[-1, :, j], p)
mask6 = cons < 0
error1[mask6] = 1e14
errors = np.hstack((error1, error2))
return errors
def test_FOC_savings(model_vars, params, expected):
# Test FOC condition for household's choice of savings
r, w, b, b_splus1, b_splus2, n, BQ, factor, T_H = model_vars
test_value = household.FOC_savings(r, w, b, b_splus1, b_splus2, n,
BQ, factor, T_H, params)
assert np.allclose(test_value, expected)
def firstdoughnutring(guesses, r, w, bq, tr, theta, factor, j, initial_b, p):
'''
Solves the first entries of the upper triangle of the twist
doughnut. This is separate from the main TPI function because the
values of b and n are scalars, so it is easier to just have a
separate function for these cases.
Args:
guesses (Numpy array): initial guesses for b and n, length 2
r (scalar): real interest rate
w (scalar): real wage rate
bq (scalar): bequest amounts by age
tr (scalar): government transfer amount
theta (Numpy array): retirement replacement rates, length J
factor (scalar): scaling factor converting model units to dollars
j (int): index of ability type
initial_b (Numpy array): savings of agents alive at T=0,
size = SxJ
p (OG-USA Specifications object): model parameters
Returns:
euler errors (Numpy array): errors from first order conditions,
length 2
'''
b_splus1 = float(guesses[0])
n = float(guesses[1])
b_s = float(initial_b[-2, j])
# Find errors from FOC for savings and FOC for labor supply
error1 = household.FOC_savings(np.array([r]), np.array([w]), b_s,
np.array([b_splus1]), np.array([n]),
np.array([bq]), factor, np.array([tr]),
theta[j], p.e[-1, j], p.rho[-1],
np.array([p.tau_c[0, -1,
j]]), p.etr_params[0,
-1, :],
p.mtry_params[0, -1, :], None, j, p,
'TPI_scalar')
error2 = household.FOC_labor(np.array([r]), np.array([w]), b_s, b_splus1,
np.array([n]), np.array([bq]), factor,
np.array([tr]), theta[j], p.chi_n[-1], p.e[-1,
j],
np.array([p.tau_c[0, -1, j]]),
p.etr_params[0, -1, :], p.mtrx_params[0,
-1, :],
None, j, p, 'TPI_scalar')
if n <= 0 or n >= 1:
error2 += 1e12
if b_splus1 <= 0:
error1 += 1e12
return [np.squeeze(error1)] + [np.squeeze(error2)]
def firstdoughnutring(guesses, r, w, bq, T_H, theta, factor, j, initial_b, p):
'''
Solves the first entries of the upper triangle of the twist
doughnut. This is separate from the main TPI function because the
values of b and n are scalars, so it is easier to just have a
separate function for these cases.
Inputs:
guesses = guess for b and n (2x1 list)
winit = initial wage rate (scalar)
rinit = initial rental rate (scalar)
BQinit = initial aggregate bequest (scalar)
T_H_init = initial lump sum tax (scalar)
initial_b = initial distribution of capital (SxJ array)
factor = steady state scaling factor (scalar)
j = which ability type is being solved for (integer)
parameters = tuple of parameters (tuple)
theta = replacement rates (Jx1 array)
tau_bq = bequest tax rates (Jx1 array)
Output:
euler errors (2x1 list)
'''
b_splus1 = float(guesses[0])
n = float(guesses[1])
b_s = float(initial_b[-2, j])
# Find errors from FOC for savings and FOC for labor supply
error1 = household.FOC_savings(np.array([r]), np.array([w]), b_s,
np.array([b_splus1]), np.array([n]),
np.array([bq]), factor, np.array([T_H]),
theta[j], p.e[-1, j], p.rho[-1],
np.array([p.tau_c[0, -1,
j]]), p.etr_params[0,
-1, :],
p.mtry_params[0, -1, :], None, j, p,
'TPI_scalar')
error2 = household.FOC_labor(np.array([r]), np.array([w]), b_s, b_splus1,
np.array([n]), np.array([bq]), factor,
np.array([T_H]), theta[j], p.chi_n[-1],
p.e[-1, j], np.array([p.tau_c[0, -1, j]]),
p.etr_params[0, -1, :], p.mtrx_params[0,
-1, :],
None, j, p, 'TPI_scalar')
if n <= 0 or n >= 1:
error2 += 1e12
if b_splus1 <= 0:
error1 += 1e12
return [np.squeeze(error1)] + [np.squeeze(error2)]
def twist_doughnut(guesses, r, w, bq, T_H, theta, factor, j, s, t, tau_c,
etr_params, mtrx_params, mtry_params, initial_b, p):
'''
Parameters:
guesses = distribution of capital and labor (various length list)
w = wage rate ((T+S)x1 array)
r = rental rate ((T+S)x1 array)
BQ = aggregate bequests ((T+S)x1 array)
T_H = 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)
'''
length = int(len(guesses) / 2)
b_guess = np.array(guesses[:length])
n_guess = np.array(guesses[length:])
if length == p.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
w_s = w[t:t + length]
r_s = r[t:t + length]
n_s = n_guess
chi_n_s = p.chi_n[-length:]
e_s = p.e[-length:, j]
rho_s = p.rho[-length:]
T_H_s = T_H[t:t + length]
error1 = household.FOC_savings(r_s, w_s, b_s, b_splus1, n_s, bq, factor,
T_H_s, theta, e_s, rho_s, tau_c, etr_params,
mtry_params, t, j, p, 'TPI')
error2 = household.FOC_labor(r_s, w_s, b_s, b_splus1, n_s, bq, factor,
T_H_s, theta, chi_n_s, e_s, tau_c, etr_params,
mtrx_params, t, j, p, 'TPI')
# Check and punish constraint violations
mask1 = n_guess < 0
error2[mask1] += 1e12
mask2 = n_guess > p.ltilde
error2[mask2] += 1e12
mask4 = b_guess <= 0
error2[mask4] += 1e12
mask5 = b_splus1 < 0
error2[mask5] += 1e12
return list(error1.flatten()) + list(error2.flatten())
def twist_doughnut(guesses, r, w, bq, tr, theta, factor, j, s, t, tau_c,
etr_params, mtrx_params, mtry_params, initial_b, p):
'''
Solves the upper triangle of time path iterations. These are the
agents who are alive at time T=0 so that we do not solve for their
full lifetime (so of their life was before the model begins).
Args:
guesses (Numpy array): initial guesses for b and n, length 2s
r (scalar): real interest rate
w (scalar): real wage rate
bq (Numpy array): bequest amounts by age, length s
tr (scalar): government transfer amount
theta (Numpy array): retirement replacement rates, length J
factor (scalar): scaling factor converting model units to dollars
j (int): index of ability type
s (int): years of life remaining
t (int): model period
tau_c (Numpy array): consumption tax rates, size = sxJ
etr_params (Numpy array): ETR function parameters,
size = sxsxnum_params
mtrx_params (Numpy array): labor income MTR function parameters,
size = sxsxnum_params
mtry_params (Numpy array): capital income MTR function
parameters, size = sxsxnum_params
initial_b (Numpy array): savings of agents alive at T=0,
size = SxJ
p (OG-USA Specifications object): model parameters
Returns:
euler errors (Numpy array): errors from first order conditions,
length 2s
'''
length = int(len(guesses) / 2)
b_guess = np.array(guesses[:length])
n_guess = np.array(guesses[length:])
if length == p.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
w_s = w[t:t + length]
r_s = r[t:t + length]
n_s = n_guess
chi_n_s = p.chi_n[-length:]
e_s = p.e[-length:, j]
rho_s = p.rho[-length:]
error1 = household.FOC_savings(r_s, w_s, b_s, b_splus1, n_s, bq, factor,
tr, theta, e_s, rho_s, tau_c, etr_params,
mtry_params, t, j, p, 'TPI')
error2 = household.FOC_labor(r_s, w_s, b_s, b_splus1, n_s, bq, factor, tr,
theta, chi_n_s, e_s, tau_c, etr_params,
mtrx_params, t, j, p, 'TPI')
# Check and punish constraint violations
mask1 = n_guess < 0
error2[mask1] += 1e12
mask2 = n_guess > p.ltilde
error2[mask2] += 1e12
mask4 = b_guess <= 0
error2[mask4] += 1e12
mask5 = b_splus1 < 0
error2[mask5] += 1e12
return list(error1.flatten()) + list(error2.flatten())
def euler_equation_solver(guesses, *args):
'''
--------------------------------------------------------------------
Finds the euler errors for certain b and n, one ability type at a time.
--------------------------------------------------------------------
INPUTS:
guesses = [2S,] vector, initial guesses for b and n
r = scalar, real interest rate
w = scalar, real wage rate
T_H = scalar, lump sum transfer
factor = scalar, scaling factor converting model units to dollars
j = integer, ability group
params = length 21 tuple, list of parameters
chi_b = [J,] vector, chi^b_j, the utility weight on bequests
chi_n = [S,] vector, chi^n_s utility weight on labor supply
tau_bq = scalar, bequest tax rate
rho = [S,] vector, mortality rates by age
lambdas = [J,] vector, fraction of population with each ability type
omega_SS = [S,] vector, stationary population weights
e = [S,J] array, effective labor units by age and ability type
tax_params = length 5 tuple, (tax_func_type, analytical_mtrs,
etr_params, mtrx_params, mtry_params)
tax_func_type = string, type of tax function used
analytical_mtrs = boolean, =True if use analytical_mtrs, =False if
use estimated MTRs
etr_params = [S,BW,#tax params] array, parameters for effective
tax rate function
mtrx_params = [S,BW,#tax params] array, parameters for marginal
tax rate on labor income function
mtry_params = [S,BW,#tax params] array, parameters for marginal
tax rate on capital income function
OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:
aggr.get_BQ()
tax.replacement_rate_vals()
household.FOC_savings()
household.FOC_labor()
tax.total_taxes()
household.get_cons()
OBJECTS CREATED WITHIN FUNCTION:
b_guess = [S,] vector, initial guess at household savings
n_guess = [S,] vector, initial guess at household labor supply
b_s = [S,] vector, wealth enter period with
b_splus1 = [S,] vector, household savings
BQ = scalar, aggregate bequests to lifetime income group
theta = scalar, replacement rate for social security benenfits
error1 = [S,] vector, errors from FOC for savings
error2 = [S,] vector, errors from FOC for labor supply
tax1 = [S,] vector, total income taxes paid
cons = [S,] vector, household consumption
RETURNS: 2Sx1 list of euler errors
OUTPUT: None
--------------------------------------------------------------------
'''
(r, w, bq, T_H, factor, j, p) = args
b_guess = np.array(guesses[:p.S])
n_guess = np.array(guesses[p.S:])
b_s = np.array([0] + list(b_guess[:-1]))
b_splus1 = b_guess
theta = tax.replacement_rate_vals(n_guess, w, factor, j, p)
error1 = household.FOC_savings(r, w, b_s, b_splus1, n_guess, bq,
factor, T_H, theta, p.e[:, j], p.rho,
p.tau_c[-1, :, j],
p.etr_params[-1, :, :],
p.mtry_params[-1, :, :], None, j, p,
'SS')
error2 = household.FOC_labor(r, w, b_s, b_splus1, n_guess, bq,
factor, T_H, theta, p.chi_n, p.e[:, j],
p.tau_c[-1, :, j],
p.etr_params[-1, :, :],
p.mtrx_params[-1, :, :], None, j, p,
'SS')
# Put in constraints for consumption and savings.
# According to the euler equations, they can be negative. When
# Chi_b is large, they will be. This prevents that from happening.
# I'm not sure if the constraints are needed for labor.
# But we might as well put them in for now.
mask1 = n_guess < 0
mask2 = n_guess > p.ltilde
mask3 = b_guess <= 0
mask4 = np.isnan(n_guess)
mask5 = np.isnan(b_guess)
error2[mask1] = 1e14
error2[mask2] = 1e14
error1[mask3] = 1e14
error1[mask5] = 1e14
error2[mask4] = 1e14
taxes = tax.total_taxes(r, w, b_s, n_guess, bq, factor, T_H, theta,
None, j, False, 'SS', p.e[:, j],
p.etr_params[-1, :, :], p)
cons = household.get_cons(r, w, b_s, b_splus1, n_guess, bq, taxes,
p.e[:, j], p.tau_c[-1, :, j], p)
mask6 = cons < 0
error1[mask6] = 1e14
return np.hstack((error1, error2))
n = pd.DataFrame(np.array(n))
w = np.array(w)
error_sum = b_s[b_s < 0].sum().sum() +\
n[n < 0].sum().sum() + w[w < 0].sum().sum()
return [np.array(1e9 * error_sum)] * 2
=======
or np.isnan(r).any() or np.isnan(w).any():
return [np.array(1e10)] * 2
>>>>>>> e45bda2b32d217b3ef4ed14b3dd87cf484ab68d3
# Find errors from FOC for savings and FOC for labor supply
error1 = household.FOC_savings(np.array([r]), np.array([w]), b_s,
np.array([b_splus1]), np.array([n]),
np.array([bq]), factor,
np.array([T_H]), theta[j],
p.e[-1, j], p.rho[-1],
np.array([p.tau_c[0, -1, j]]),
p.etr_params[0, -1, :],
p.mtry_params[0, -1, :], None, j, p,
'TPI_scalar')
error2 = household.FOC_labor(
np.array([r]), np.array([w]), b_s, b_splus1, np.array([n]),
np.array([bq]), factor, np.array([T_H]), theta[j], p.chi_n[-1],
p.e[-1, j], np.array([p.tau_c[0, -1, j]]), p.etr_params[0, -1, :],
p.mtrx_params[0, -1, :], None, j, p, 'TPI_scalar')
if n <= 0 or n >= 1:
error2 += 1e12
if b_splus1 <= 0:
error1 += 1e12
