"""
------------------------------------------------------------------------
Household functions.
------------------------------------------------------------------------
"""
# Packages
import numpy as np
from ogcore import tax, utils
"""
------------------------------------------------------------------------
Functions
------------------------------------------------------------------------
"""
[docs]
def marg_ut_cons(c, sigma):
r"""
Compute the marginal utility of consumption.
.. math::
MU_{c} = c^{-\sigma}
Args:
c (array_like): household consumption
sigma (scalar): coefficient of relative risk aversion
Returns:
output (array_like): marginal utility of consumption
"""
if np.ndim(c) == 0:
c = np.array([c])
epsilon = 0.003
cvec_cnstr = c < epsilon
MU_c = np.zeros(c.shape)
MU_c[~cvec_cnstr] = c[~cvec_cnstr] ** (-sigma)
b2 = (-sigma * (epsilon ** (-sigma - 1))) / 2
b1 = (epsilon ** (-sigma)) - 2 * b2 * epsilon
MU_c[cvec_cnstr] = 2 * b2 * c[cvec_cnstr] + b1
output = MU_c
output = np.squeeze(output)
return output
[docs]
def marg_ut_labor(n, chi_n, p):
r"""
Compute the marginal disutility of labor.
.. math::
MDU_{l} = \chi^n_{s}\biggl(\frac{b}{\tilde{l}}\biggr)
\biggl(\frac{n_{j,s,t}}{\tilde{l}}\biggr)^{\upsilon-1}
\Biggl[1-\biggl(\frac{n_{j,s,t}}{\tilde{l}}\biggr)^\upsilon
\Biggr]^{\frac{1-\upsilon}{\upsilon}}
Args:
n (array_like): household labor supply
chi_n (array_like): utility weights on disutility of labor
p (OG-Core Specifications object): model parameters
Returns:
output (array_like): marginal disutility of labor supply
"""
nvec = n
if np.ndim(nvec) == 0:
nvec = np.array([nvec])
eps_low = 0.000001
eps_high = p.ltilde - 0.000001
nvec_low = nvec < eps_low
nvec_high = nvec > eps_high
nvec_uncstr = np.logical_and(~nvec_low, ~nvec_high)
MDU_n = np.zeros(nvec.shape)
MDU_n[nvec_uncstr] = (
(p.b_ellipse / p.ltilde)
* ((nvec[nvec_uncstr] / p.ltilde) ** (p.upsilon - 1))
* (
(1 - ((nvec[nvec_uncstr] / p.ltilde) ** p.upsilon))
** ((1 - p.upsilon) / p.upsilon)
)
)
b2 = (
0.5
* p.b_ellipse
* (p.ltilde ** (-p.upsilon))
* (p.upsilon - 1)
* (eps_low ** (p.upsilon - 2))
* (
(1 - ((eps_low / p.ltilde) ** p.upsilon))
** ((1 - p.upsilon) / p.upsilon)
)
* (
1
+ ((eps_low / p.ltilde) ** p.upsilon)
* ((1 - ((eps_low / p.ltilde) ** p.upsilon)) ** (-1))
)
)
b1 = (p.b_ellipse / p.ltilde) * (
(eps_low / p.ltilde) ** (p.upsilon - 1)
) * (
(1 - ((eps_low / p.ltilde) ** p.upsilon))
** ((1 - p.upsilon) / p.upsilon)
) - (
2 * b2 * eps_low
)
MDU_n[nvec_low] = 2 * b2 * nvec[nvec_low] + b1
d2 = (
0.5
* p.b_ellipse
* (p.ltilde ** (-p.upsilon))
* (p.upsilon - 1)
* (eps_high ** (p.upsilon - 2))
* (
(1 - ((eps_high / p.ltilde) ** p.upsilon))
** ((1 - p.upsilon) / p.upsilon)
)
* (
1
+ ((eps_high / p.ltilde) ** p.upsilon)
* ((1 - ((eps_high / p.ltilde) ** p.upsilon)) ** (-1))
)
)
d1 = (p.b_ellipse / p.ltilde) * (
(eps_high / p.ltilde) ** (p.upsilon - 1)
) * (
(1 - ((eps_high / p.ltilde) ** p.upsilon))
** ((1 - p.upsilon) / p.upsilon)
) - (
2 * d2 * eps_high
)
MDU_n[nvec_high] = 2 * d2 * nvec[nvec_high] + d1
output = MDU_n * np.squeeze(chi_n)
output = np.squeeze(output)
return output
[docs]
def get_bq(BQ, j, p, method):
r"""
Calculate bequests to each household.
.. math::
\hat{bq}_{j,s,t} = \zeta_{j,s}
\frac{\hat{BQ}_{t}}{\lambda_{j}\hat{\omega}_{s,t}} \quad\forall j,s,t
Args:
BQ (array_like): aggregate bequests
j (int): index of lifetime ability group
p (OG-Core Specifications object): model parameters
method (str): adjusts calculation dimensions based on 'SS' or
'TPI'
Returns:
bq (array_like): bequests received by each household
"""
if p.use_zeta:
if j is not None:
if method == "SS":
bq = (p.zeta[:, j] * BQ) / (p.lambdas[j] * p.omega_SS)
else:
len_T = BQ.shape[0]
bq = (
np.reshape(p.zeta[:, j], (1, p.S)) * BQ.reshape((len_T, 1))
) / (p.lambdas[j] * p.omega[:len_T, :])
else:
if method == "SS":
bq = (p.zeta * BQ) / (
p.lambdas.reshape((1, p.J)) * p.omega_SS.reshape((p.S, 1))
)
else:
len_T = BQ.shape[0]
bq = (
np.reshape(p.zeta, (1, p.S, p.J))
* utils.to_timepath_shape(BQ)
) / (
p.lambdas.reshape((1, 1, p.J))
* p.omega[:len_T, :].reshape((len_T, p.S, 1))
)
else:
if j is not None:
if method == "SS":
bq = np.tile(BQ[j], p.S) / p.lambdas[j]
if method == "TPI":
len_T = BQ.shape[0]
bq = np.tile(
np.reshape(BQ[:, j] / p.lambdas[j], (len_T, 1)), (1, p.S)
)
else:
if method == "SS":
BQ_per = BQ / np.squeeze(p.lambdas)
bq = np.tile(np.reshape(BQ_per, (1, p.J)), (p.S, 1))
if method == "TPI":
len_T = BQ.shape[0]
BQ_per = BQ / p.lambdas.reshape(1, p.J)
bq = np.tile(np.reshape(BQ_per, (len_T, 1, p.J)), (1, p.S, 1))
return bq
[docs]
def get_tr(TR, j, p, method):
r"""
Calculate transfers to each household.
.. math::
\hat{tr}_{j,s,t} = \zeta_{j,s}
\frac{\hat{TR}_{t}}{\lambda_{j}\hat{\omega}_{s,t}} \quad\forall j,s,t
Args:
TR (array_like): aggregate transfers
j (int): index of lifetime ability group
p (OG-Core Specifications object): model parameters
method (str): adjusts calculation dimensions based on 'SS' or
'TPI'
Returns:
tr (array_like): transfers received by household
"""
if j is not None:
if method == "SS":
tr = (p.eta[-1, :, j] * TR) / (p.lambdas[j] * p.omega_SS)
else:
len_T = TR.shape[0]
tr = (p.eta[:len_T, :, j] * TR.reshape((len_T, 1))) / (
p.lambdas[j] * p.omega[:len_T, :]
)
else:
if method == "SS":
tr = (p.eta[-1, :, :] * TR) / (
p.lambdas.reshape((1, p.J)) * p.omega_SS.reshape((p.S, 1))
)
else:
len_T = TR.shape[0]
tr = (p.eta[:len_T, :, :] * utils.to_timepath_shape(TR)) / (
p.lambdas.reshape((1, 1, p.J))
* p.omega[:len_T, :].reshape((len_T, p.S, 1))
)
return tr
[docs]
def get_rm(RM, j, p, method):
r"""
Calculate remittances to each household.
.. math::
\hat{rm}_{j,s,t} = \eta_{RM,j,s,t}
\frac{\hat{RM}_{t}}{\lambda_{j}\hat{\omega}_{s,t}} \quad\forall j,s,t
Args:
RM (array_like): aggregate remittances
j (int): index of lifetime ability group
p (OG-Core Specifications object): model parameters
method (str): adjusts calculation dimensions based on 'SS' or
'TPI'
Returns:
rm (array_like): remittances received by household
"""
if j is not None:
if method == "SS":
rm = (p.eta_RM[-1, :, j] * RM) / (p.lambdas[j] * p.omega_SS)
else:
len_T = RM.shape[0]
rm = (p.eta_RM[:len_T, :, j] * RM.reshape((len_T, 1))) / (
p.lambdas[j] * p.omega[:len_T, :]
)
else:
if method == "SS":
rm = (p.eta_RM[-1, :, :] * RM) / (
p.lambdas.reshape((1, p.J)) * p.omega_SS.reshape((p.S, 1))
)
else:
len_T = RM.shape[0]
rm = (p.eta_RM[:len_T, :, :] * utils.to_timepath_shape(RM)) / (
p.lambdas.reshape((1, 1, p.J))
* p.omega[:len_T, :].reshape((len_T, p.S, 1))
)
return rm
[docs]
def get_cons(r_p, w, p_tilde, b, b_splus1, n, bq, rm, net_tax, e, p):
r"""
Calculate household composite consumption.
.. math::
\hat{c}_{j,s,t} &= \biggl[(1 + r_{p,t})\hat{b}_{j,s,t}
+ \hat{w}_t e_{j,s}n_{j,s,t} + \hat{bq}_{j,s,t} + \hat{rm}_{j,s,t}
+ \hat{tr}_{j,s,t} + \hat{ubi}_{j,s,t} + \hat{pension}_{j,s,t}
- \hat{tax}_{j,s,t} \\
&\qquad - \sum_{i=1}^I\left(1 + \tau^c_{i,t}\right)p_{i,t}\hat{c}_{min,i}
- e^{g_y}\hat{b}_{j,s+1,t+1}\biggr] / p_t \\
&\qquad\qquad\forall j,t \quad\text{and}\quad E+1\leq s\leq E+S
\quad\text{where}\quad \hat{b}_{j,E+1,t}=0
Args:
r_p (array_like): the real interest rate
w (array_like): the real wage rate
p_tilde (array_like): the ratio of real GDP to nominal GDP
b (Numpy array): household savings
b_splus1 (Numpy array): household savings one period ahead
n (Numpy array): household labor supply
bq (Numpy array): household bequests received
rm (Numpy array): household remittances received
net_tax (Numpy array): household net taxes paid
e (Numpy array): effective labor units
p (OG-Core Specifications object): model parameters
Returns:
cons (Numpy array): household consumption
"""
cons = (
(1 + r_p) * b
+ w * e * n
+ bq
+ rm
- net_tax
- b_splus1 * np.exp(p.g_y)
) / p_tilde
return cons
[docs]
def get_ci(c_s, p_i, p_tilde, tau_c, alpha_c, method="SS"):
r"""
Compute consumption of good i given amount of composite consumption
and prices.
.. math::
c_{i,j,s,t} = \frac{c_{s,j,t}}{\alpha_{i,j}p_{i,j}}
Args:
c_s (array_like): composite consumption
p_i (array_like): prices for consumption good i
p_tilde (array_like): composite good price
tau_c (array_like): consumption tax rate
alpha_c (array_like): consumption share parameters
method (str): adjusts calculation dimensions based on 'SS' or 'TPI'
Returns:
c_si (array_like): consumption of good i
"""
if method == "SS":
I = alpha_c.shape[0]
S = c_s.shape[0]
J = c_s.shape[1]
tau_c = tau_c.reshape(I, 1, 1)
alpha_c = alpha_c.reshape(I, 1, 1)
p_tilde.reshape(1, 1, 1)
p_i = p_i.reshape(I, 1, 1)
c_s = c_s.reshape(1, S, J)
c_si = alpha_c * (((1 + tau_c) * p_i) / p_tilde) ** (-1) * c_s
else: # Time path case
I = alpha_c.shape[0]
T = p_i.shape[0]
S = c_s.shape[1]
J = c_s.shape[2]
tau_c = tau_c.reshape(T, I, 1, 1)
alpha_c = alpha_c.reshape(1, I, 1, 1)
p_tilde = p_tilde.reshape(T, 1, 1, 1)
p_i = p_i.reshape(T, I, 1, 1)
c_s = c_s.reshape(T, 1, S, J)
c_si = alpha_c * (((1 + tau_c) * p_i) / p_tilde) ** (-1) * c_s
return c_si
[docs]
def FOC_savings(
r,
w,
p_tilde,
b,
b_splus1,
n,
bq,
rm,
factor,
tr,
ubi,
theta,
rho,
etr_params,
mtry_params,
t,
j,
p,
method,
):
r"""
Computes Euler errors for the FOC for savings in the steady state.
This function is usually looped through over J, so it does one
lifetime income group at a time.
.. math::
\frac{c_{j,s,t}^{-\sigma}}{\tilde{p}_{t}} = e^{-\sigma g_y}
\biggl[\chi^b_j\rho_s(b_{j,s+1,t+1})^{-\sigma} +
\beta_j\bigl(1 - \rho_s\bigr)\Bigl(\frac{1 + r_{t+1}
\bigl[1 - \tau^{mtry}_{s+1,t+1}\bigr]}{\tilde{p}_{t+1}}\Bigr)
(c_{j,s+1,t+1})^{-\sigma}\biggr]
Args:
r (array_like): the real interest rate
w (array_like): the real wage rate
p_tilde (array_like): composite good price
b (Numpy array): household savings
b_splus1 (Numpy array): household savings one period ahead
b_splus2 (Numpy array): household savings two periods ahead
n (Numpy array): household labor supply
bq (Numpy array): household bequests received
rm (Numpy array): household remittances received
factor (scalar): scaling factor converting model units to dollars
tr (Numpy array): government transfers to household
ubi (Numpy array): universal basic income payment
theta (Numpy array): social security replacement rate for each
lifetime income group
rho (Numpy array): mortality rates
etr_params (list): parameters of the effective tax rate
functions
mtry_params (list): parameters of the marginal tax rate
on capital income functions
t (int): model period
j (int): index of ability type
p (OG-Core Specifications object): model parameters
method (str): adjusts calculation dimensions based on 'SS' or
'TPI'
Returns:
euler (Numpy array): Euler error from FOC for savings
"""
if j is not None:
chi_b = p.chi_b[j]
beta = p.beta[j]
if method == "SS":
tax_noncompliance = p.capital_income_tax_noncompliance_rate[-1, j]
e = np.squeeze(p.e[-1, :, j])
elif method == "TPI_scalar":
tax_noncompliance = p.capital_income_tax_noncompliance_rate[0, j]
e = np.squeeze(p.e[0, :, j])
else:
length = r.shape[0]
tax_noncompliance = p.capital_income_tax_noncompliance_rate[
t : t + length, j
]
e_long = np.concatenate(
(
p.e,
np.tile(p.e[-1, :, :].reshape(1, p.S, p.J), (p.S, 1, 1)),
),
axis=0,
)
e = np.diag(e_long[t : t + p.S, :, j], max(p.S - length, 0))
else:
chi_b = p.chi_b
beta = p.beta
if method == "SS":
tax_noncompliance = p.capital_income_tax_noncompliance_rate[-1, :]
e = np.squeeze(p.e[-1, :, :])
elif method == "TPI_scalar":
tax_noncompliance = p.capital_income_tax_noncompliance_rate[0, :]
e = np.squeeze(p.e[0, :, :])
else:
length = r.shape[0]
tax_noncompliance = p.capital_income_tax_noncompliance_rate[
t : t + length, :
]
e_long = np.concatenate(
(
p.e,
np.tile(p.e[-1, :, :].reshape(1, p.S, p.J), (p.S, 1, 1)),
),
axis=0,
)
e = np.diag(e_long[t : t + p.S, :, :], max(p.S - length, 0))
e = np.squeeze(e)
if method == "SS":
h_wealth = p.h_wealth[-1]
m_wealth = p.m_wealth[-1]
p_wealth = p.p_wealth[-1]
p_tilde = np.ones_like(p.rho[-1, :]) * p_tilde
elif method == "TPI_scalar":
h_wealth = p.h_wealth[0]
m_wealth = p.m_wealth[0]
p_wealth = p.p_wealth[0]
else:
h_wealth = p.h_wealth[t]
m_wealth = p.m_wealth[t]
p_wealth = p.p_wealth[t]
taxes = tax.net_taxes(
r,
w,
b,
n,
bq,
factor,
tr,
ubi,
theta,
t,
j,
False,
method,
e,
etr_params,
p,
)
cons = get_cons(r, w, p_tilde, b, b_splus1, n, bq, rm, taxes, e, p)
deriv = (
(1 + r)
- (
r
* tax.MTR_income(
r,
w,
b,
n,
factor,
True,
e,
etr_params,
mtry_params,
tax_noncompliance,
p,
)
)
- tax.MTR_wealth(b, h_wealth, m_wealth, p_wealth)
)
savings_ut = (
rho * np.exp(-p.sigma * p.g_y) * chi_b * b_splus1 ** (-p.sigma)
)
euler_error = np.zeros_like(n)
if n.shape[0] > 1:
euler_error[:-1] = (
marg_ut_cons(cons[:-1], p.sigma) * (1 / p_tilde[:-1])
- beta
* (1 - rho[:-1])
* deriv[1:]
* marg_ut_cons(cons[1:], p.sigma)
* (1 / p_tilde[1:])
* np.exp(-p.sigma * p.g_y)
- savings_ut[:-1]
)
euler_error[-1] = (
marg_ut_cons(cons[-1], p.sigma) * (1 / p_tilde[-1])
- savings_ut[-1]
)
else:
euler_error[-1] = (
marg_ut_cons(cons[-1], p.sigma) * (1 / p_tilde[-1])
- savings_ut[-1]
)
return euler_error
[docs]
def FOC_labor(
r,
w,
p_tilde,
b,
b_splus1,
n,
bq,
rm,
factor,
tr,
ubi,
theta,
chi_n,
etr_params,
mtrx_params,
t,
j,
p,
method,
):
r"""
Computes errors for the FOC for labor supply in the steady
state. This function is usually looped through over J, so it does
one lifetime income group at a time.
.. math::
w_t e_{j,s}\bigl(1 - \tau^{mtrx}_{s,t}\bigr)
\frac{(c_{j,s,t})^{-\sigma}}{ \tilde{p}_{t}} = \chi^n_{s}
\biggl(\frac{b}{\tilde{l}}\biggr)\biggl(\frac{n_{j,s,t}}
{\tilde{l}}\biggr)^{\upsilon-1}\Biggl[1 -
\biggl(\frac{n_{j,s,t}}{\tilde{l}}\biggr)^\upsilon\Biggr]
^{\frac{1-\upsilon}{\upsilon}}
Args:
r (array_like): the real interest rate
w (array_like): the real wage rate
p_tilde (array_like): composite good price
b (Numpy array): household savings
b_splus1 (Numpy array): household savings one period ahead
n (Numpy array): household labor supply
bq (Numpy array): household bequests received
rm (Numpy array): household bequests received
factor (scalar): scaling factor converting model units to dollars
tr (Numpy array): government transfers to household
ubi (Numpy array): universal basic income payment
theta (Numpy array): social security replacement rate for each
lifetime income group
chi_n (Numpy array): utility weight on the disutility of labor
supply
e (Numpy array): effective labor units
etr_params (list): parameters of the effective tax rate
functions
mtrx_params (list): parameters of the marginal tax rate
on labor income functions
t (int): model period
j (int): index of ability type
p (OG-Core Specifications object): model parameters
method (str): adjusts calculation dimensions based on 'SS' or
'TPI'
Returns:
FOC_error (Numpy array): error from FOC for labor supply
"""
if method == "SS":
tau_payroll = p.tau_payroll[-1]
elif method == "TPI_scalar": # for 1st donut ring only
tau_payroll = p.tau_payroll[0]
else:
length = r.shape[0]
tau_payroll = p.tau_payroll[t : t + length]
if j is not None:
if method == "SS":
tax_noncompliance = p.labor_income_tax_noncompliance_rate[-1, j]
e = np.squeeze(p.e[-1, :, j])
elif method == "TPI_scalar":
tax_noncompliance = p.labor_income_tax_noncompliance_rate[0, j]
e = np.squeeze(p.e[0, -1, j])
else:
tax_noncompliance = p.labor_income_tax_noncompliance_rate[
t : t + length, j
]
e_long = np.concatenate(
(
p.e,
np.tile(p.e[-1, :, :].reshape(1, p.S, p.J), (p.S, 1, 1)),
),
axis=0,
)
e = np.diag(e_long[t : t + p.S, :, j], max(p.S - length, 0))
else:
if method == "SS":
tax_noncompliance = p.labor_income_tax_noncompliance_rate[-1, :]
e = np.squeeze(p.e[-1, :, :])
elif method == "TPI_scalar":
tax_noncompliance = p.labor_income_tax_noncompliance_rate[0, :]
e = np.squeeze(p.e[0, -1, :])
else:
tax_noncompliance = p.labor_income_tax_noncompliance_rate[
t : t + length, :
]
e_long = np.concatenate(
(
p.e,
np.tile(p.e[-1, :, :].reshape(1, p.S, p.J), (p.S, 1, 1)),
),
axis=0,
)
e = np.diag(e_long[t : t + p.S, :, j], max(p.S - length, 0))
if method == "SS":
tau_payroll = p.tau_payroll[-1]
elif method == "TPI_scalar": # for 1st donut ring only
tau_payroll = p.tau_payroll[0]
else:
length = r.shape[0]
tau_payroll = p.tau_payroll[t : t + length]
if method == "TPI":
if b.ndim == 2:
r = r.reshape(r.shape[0], 1)
w = w.reshape(w.shape[0], 1)
tau_payroll = tau_payroll.reshape(tau_payroll.shape[0], 1)
taxes = tax.net_taxes(
r,
w,
b,
n,
bq,
factor,
tr,
ubi,
theta,
t,
j,
False,
method,
e,
etr_params,
p,
)
cons = get_cons(r, w, p_tilde, b, b_splus1, n, bq, rm, taxes, e, p)
deriv = (
1
- tau_payroll
- tax.MTR_income(
r,
w,
b,
n,
factor,
False,
e,
etr_params,
mtrx_params,
tax_noncompliance,
p,
)
)
FOC_error = marg_ut_cons(cons, p.sigma) * (
1 / p_tilde
) * w * deriv * e - marg_ut_labor(n, chi_n, p)
return FOC_error
[docs]
def get_y(r_p, w, b_s, n, p, method):
r"""
Compute household income before taxes.
.. math::
y_{j,s,t} = r_{p,t}b_{j,s,t} + w_{t}e_{j,s}n_{j,s,t}
Args:
r_p (array_like): real interest rate on the household portfolio
w (array_like): real wage rate
b_s (Numpy array): household savings coming into the period
n (Numpy array): household labor supply
p (OG-Core Specifications object): model parameters
method (str): adjusts calculation dimensions based on 'SS' or 'TPI'
"""
if method == "SS":
e = np.squeeze(p.e[-1, :, :])
elif method == "TPI":
e = p.e
y = r_p * b_s + w * e * n
return y
[docs]
def constraint_checker_SS(bssmat, nssmat, cssmat, ltilde):
"""
Checks constraints on consumption, savings, and labor supply in the
steady state.
Args:
bssmat (Numpy array): steady state distribution of capital
nssmat (Numpy array): steady state distribution of labor
cssmat (Numpy array): steady state distribution of consumption
ltilde (scalar): upper bound of household labor supply
Returns:
None
Raises:
Warnings: if constraints are violated, warnings printed
"""
print("Checking constraints on capital, labor, and consumption.")
if (bssmat < 0).any():
print("\tWARNING: There is negative capital stock")
flag2 = False
if (nssmat < 0).any():
print(
"\tWARNING: Labor supply violates nonnegativity ", "constraints."
)
flag2 = True
if (nssmat > ltilde).any():
print("\tWARNING: Labor supply violates the ltilde constraint.")
flag2 = True
if flag2 is False:
print(
"\tThere were no violations of the constraints on labor",
" supply.",
)
if (cssmat < 0).any():
print("\tWARNING: Consumption violates nonnegativity", " constraints.")
else:
print(
"\tThere were no violations of the constraints on", " consumption."
)
[docs]
def constraint_checker_TPI(b_dist, n_dist, c_dist, t, ltilde):
"""
Checks constraints on consumption, savings, and labor supply along
the transition path. Does this for each period t separately.
Args:
b_dist (Numpy array): distribution of capital at time t
n_dist (Numpy array): distribution of labor at time t
c_dist (Numpy array): distribution of consumption at time t
t (int): time period
ltilde (scalar): upper bound of household labor supply
Returns:
None
Raises:
Warnings: if constraints are violated, warnings printed
"""
if (b_dist <= 0).any():
print(
"\tWARNING: Aggregate capital is less than or equal to ",
"zero in period %.f." % t,
)
if (n_dist < 0).any():
print(
"\tWARNING: Labor supply violates nonnegativity",
" constraints in period %.f." % t,
)
if (n_dist > ltilde).any():
print(
"\tWARNING: Labor suppy violates the ltilde constraint",
" in period %.f." % t,
)
if (c_dist < 0).any():
print(
"\tWARNING: Consumption violates nonnegativity",
" constraints in period %.f." % t,
)
