def test_euler_equation_solver():
# Test SS.inner_loop function. Provide inputs to function and
# ensure that output returned matches what it has been before.
input_tuple = utils.safe_read_pickle(
os.path.join(CUR_PATH, 'test_io_data', 'euler_eqn_solver_inputs.pkl'))
(guesses, params) = input_tuple
p = Specifications()
(r, w, TR, factor, j, p.J, p.S, p.beta, p.sigma, p.ltilde, p.g_y,
p.g_n_ss, tau_payroll, retire, p.mean_income_data, h_wealth,
p_wealth, m_wealth, p.b_ellipse, p.upsilon, j, p.chi_b,
p.chi_n, tau_bq, p.rho, lambdas, p.omega_SS, p.e,
p.analytical_mtrs, etr_params, mtrx_params, mtry_params) = params
p.eta = (p.omega_SS.reshape(p.S, 1) *
p.lambdas.reshape(1, p.J)).reshape(1, p.S, p.J)
p.tau_bq = np.ones(p.T + p.S) * 0.0
p.tau_payroll = np.ones(p.T + p.S) * tau_payroll
p.h_wealth = np.ones(p.T + p.S) * h_wealth
p.p_wealth = np.ones(p.T + p.S) * p_wealth
p.m_wealth = np.ones(p.T + p.S) * m_wealth
p.retire = (np.ones(p.T + p.S) * retire).astype(int)
p.etr_params = np.transpose(etr_params.reshape(
p.S, 1, etr_params.shape[-1]), (1, 0, 2))
p.mtrx_params = np.transpose(mtrx_params.reshape(
p.S, 1, mtrx_params.shape[-1]), (1, 0, 2))
p.mtry_params = np.transpose(mtry_params.reshape(
p.S, 1, mtry_params.shape[-1]), (1, 0, 2))
p.tax_func_type = 'DEP'
p.lambdas = lambdas.reshape(p.J, 1)
b_splus1 = np.array(guesses[:p.S]).reshape(p.S, 1) + 0.005
BQ = aggregates.get_BQ(r, b_splus1, j, p, 'SS', False)
bq = household.get_bq(BQ, j, p, 'SS')
tr = household.get_tr(TR, j, p, 'SS')
args = (r, w, bq, tr, factor, j, p)
test_list = SS.euler_equation_solver(guesses, *args)
expected_list = np.array([
-3.62741663e+00, -6.30068841e+00, -6.76592886e+00,
-6.97731223e+00, -7.05777777e+00, -6.57305440e+00,
-7.11553046e+00, -7.30569622e+00, -7.45808107e+00,
-7.89984062e+00, -8.11466111e+00, -8.28230086e+00,
-8.79253862e+00, -8.86994311e+00, -9.31299476e+00,
-9.80834199e+00, -9.97333771e+00, -1.08349979e+01,
-1.13199826e+01, -1.22890930e+01, -1.31550471e+01,
-1.42753713e+01, -1.55721098e+01, -1.73811490e+01,
-1.88856303e+01, -2.09570569e+01, -2.30559500e+01,
-2.52127149e+01, -2.76119605e+01, -3.03141128e+01,
-3.30900203e+01, -3.62799730e+01, -3.91169706e+01,
-4.24246421e+01, -4.55740527e+01, -4.92914871e+01,
-5.30682805e+01, -5.70043846e+01, -6.06075991e+01,
-6.45251018e+01, -6.86128365e+01, -7.35896515e+01,
-7.92634608e+01, -8.34733231e+01, -9.29802390e+01,
-1.01179788e+02, -1.10437881e+02, -1.20569527e+02,
-1.31569973e+02, -1.43633399e+02, -1.57534056e+02,
-1.73244610e+02, -1.90066728e+02, -2.07980863e+02,
-2.27589046e+02, -2.50241670e+02, -2.76314755e+02,
-3.04930986e+02, -3.36196973e+02, -3.70907934e+02,
-4.10966644e+02, -4.56684022e+02, -5.06945218e+02,
-5.61838645e+02, -6.22617808e+02, -6.90840503e+02,
-7.67825713e+02, -8.54436805e+02, -9.51106365e+02,
-1.05780305e+03, -1.17435473e+03, -1.30045062e+03,
-1.43571221e+03, -1.57971603e+03, -1.73204264e+03,
-1.88430524e+03, -2.03403679e+03, -2.17861987e+03,
-2.31532884e+03, -8.00654731e+03, -5.21487172e-02,
-2.80234170e-01, 4.93894552e-01, 3.11884938e-01, 6.55799607e-01,
5.62182419e-01, 3.86074983e-01, 3.43741491e-01, 4.22461089e-01,
3.63707951e-01, 4.93150010e-01, 4.72813688e-01, 4.07390308e-01,
4.94974186e-01, 4.69900128e-01, 4.37562389e-01, 5.67370182e-01,
4.88965362e-01, 6.40728461e-01, 6.14619979e-01, 4.97173823e-01,
6.19549666e-01, 6.51193557e-01, 4.48906118e-01, 7.93091492e-01,
6.51249363e-01, 6.56307713e-01, 1.12948552e+00, 9.50018058e-01,
6.79613030e-01, 9.51359123e-01, 6.31059147e-01, 7.97896887e-01,
8.44620817e-01, 7.43683837e-01, 1.56693187e+00, 2.75630011e-01,
5.32956891e-01, 1.57110727e+00, 1.22674610e+00, 4.63932928e-01,
1.47225464e+00, 1.16948107e+00, 1.07965795e+00, -3.20557791e-01,
-1.17064127e+00, -7.84880649e-01, -7.60851182e-01, -1.61415945e+00,
-8.30363975e-01, -1.68459409e+00, -1.49260581e+00, -1.84257084e+00,
-1.72143079e+00, -1.43131579e+00, -1.63719219e+00, -1.43874851e+00,
-1.57207905e+00, -1.72909159e+00, -1.98778122e+00, -1.80843826e+00,
-2.12828312e+00, -2.24768762e+00, -2.36961877e+00, -2.49117258e+00,
-2.59914065e+00, -2.82309085e+00, -2.93613362e+00, -3.34446991e+00,
-3.45445086e+00, -3.74962140e+00, -3.78113417e+00, -4.55643800e+00,
-4.86929016e+00, -5.08657898e+00, -5.22054177e+00, -5.54606515e+00,
-5.78478304e+00, -5.93652041e+00, -6.11519786e+00])
assert(np.allclose(np.array(test_list), np.array(expected_list)))
def test_get_tr(TR, j, p, method, expected):
# Test the get_tr function
test_value = household.get_tr(TR, j, p, method)
print('Test value = ', test_value)
assert np.allclose(test_value, expected)
def run_TPI(p, client=None):
'''
Solve for transition path equilibrium of OG-India.
Args:
p (OG-India Specifcations object): model parameters
client (Dask client object): client
Returns:
output (dictionary): dictionary with transition path solution
results
'''
# unpack tuples of parameters
initial_values, ss_vars, theta, baseline_values = get_initial_SS_values(p)
(B0, b_sinit, b_splus1init, factor, initial_b, initial_n,
D0) = initial_values
(TRbaseline, Gbaseline) = baseline_values
print('Government spending breakpoints are tG1: ', p.tG1, '; and tG2:',
p.tG2)
# Initialize guesses at time paths
# Make array of initial guesses for labor supply and savings
domain = np.linspace(0, p.T, p.T)
domain2 = np.tile(domain.reshape(p.T, 1, 1), (1, p.S, p.J))
ending_b = ss_vars['bssmat_splus1']
guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
ending_b_tail = np.tile(ending_b.reshape(1, p.S, p.J), (p.S, 1, 1))
guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
domain3 = np.tile(np.linspace(0, 1, p.T).reshape(p.T, 1, 1), (1, p.S, p.J))
guesses_n = domain3 * (ss_vars['nssmat'] - initial_n) + initial_n
ending_n_tail = np.tile(ss_vars['nssmat'].reshape(1, p.S, p.J),
(p.S, 1, 1))
guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
b_mat = guesses_b
n_mat = guesses_n
ind = np.arange(p.S)
L_init = np.ones((p.T + p.S, )) * ss_vars['Lss']
B_init = np.ones((p.T + p.S, )) * ss_vars['Bss']
L_init[:p.T] = aggr.get_L(n_mat[:p.T], p, 'TPI')
B_init[1:p.T] = aggr.get_B(b_mat[:p.T], p, 'TPI', False)[:p.T - 1]
B_init[0] = B0
if not p.small_open:
if p.budget_balance:
K_init = B_init
else:
K_init = B_init * ss_vars['Kss'] / ss_vars['Bss']
else:
K_init = firm.get_B(L_init, p.firm_r, p, 'TPI')
K = K_init
K_d = K_init * ss_vars['K_d_ss'] / ss_vars['Kss']
K_f = K_init * ss_vars['K_f_ss'] / ss_vars['Kss']
L = L_init
B = B_init
Y = np.zeros_like(K)
Y[:p.T] = firm.get_Y(K[:p.T], L[:p.T], p, 'TPI')
Y[p.T:] = ss_vars['Yss']
r = np.zeros_like(Y)
if not p.small_open:
r[:p.T] = firm.get_r(Y[:p.T], K[:p.T], p, 'TPI')
r[p.T:] = ss_vars['rss']
else:
r = p.firm_r
# compute w
w = np.zeros_like(r)
w[:p.T] = firm.get_w_from_r(r[:p.T], p, 'TPI')
w[p.T:] = ss_vars['wss']
r_gov = fiscal.get_r_gov(r, p)
if p.budget_balance:
r_hh = r
else:
r_hh = aggr.get_r_hh(r, r_gov, K, ss_vars['Dss'])
if p.small_open:
r_hh = p.hh_r
BQ0 = aggr.get_BQ(r[0], initial_b, None, p, 'SS', True)
if not p.use_zeta:
BQ = np.zeros((p.T + p.S, p.J))
for j in range(p.J):
BQ[:, j] = (list(np.linspace(BQ0[j], ss_vars['BQss'][j], p.T)) +
[ss_vars['BQss'][j]] * p.S)
BQ = np.array(BQ)
else:
BQ = (list(np.linspace(BQ0, ss_vars['BQss'], p.T)) +
[ss_vars['BQss']] * p.S)
BQ = np.array(BQ)
if p.budget_balance:
if np.abs(ss_vars['TR_ss']) < 1e-13:
TR_ss2 = 0.0 # sometimes SS is very small but not zero,
# even if taxes are zero, this get's rid of the approximation
# error, which affects the perc changes below
else:
TR_ss2 = ss_vars['TR_ss']
TR = np.ones(p.T + p.S) * TR_ss2
total_revenue = TR
G = np.zeros(p.T + p.S)
elif not p.baseline_spending:
TR = p.alpha_T * Y
G = np.ones(p.T + p.S) * ss_vars['Gss']
elif p.baseline_spending:
TR = TRbaseline
TR_new = p.TR # Need to set TR_new for later reference
G = Gbaseline
G_0 = Gbaseline[0]
# Initialize some starting values
if p.budget_balance:
D = np.zeros(p.T + p.S)
else:
D = np.ones(p.T + p.S) * ss_vars['Dss']
if ss_vars['Dss'] == 0:
D_d = np.zeros(p.T + p.S)
D_f = np.zeros(p.T + p.S)
else:
D_d = D * ss_vars['D_d_ss'] / ss_vars['Dss']
D_f = D * ss_vars['D_f_ss'] / ss_vars['Dss']
total_revenue = np.ones(p.T + p.S) * ss_vars['total_revenue_ss']
TPIiter = 0
TPIdist = 10
euler_errors = np.zeros((p.T, 2 * p.S, p.J))
TPIdist_vec = np.zeros(p.maxiter)
# TPI loop
while (TPIiter < p.maxiter) and (TPIdist >= p.mindist_TPI):
r_gov[:p.T] = fiscal.get_r_gov(r[:p.T], p)
if p.budget_balance:
r_hh[:p.T] = r[:p.T]
else:
K[:p.T] = firm.get_K_from_Y(Y[:p.T], r[:p.T], p, 'TPI')
r_hh[:p.T] = aggr.get_r_hh(r[:p.T], r_gov[:p.T], K[:p.T], D[:p.T])
if p.small_open:
r_hh[:p.T] = p.hh_r[:p.T]
outer_loop_vars = (r, w, r_hh, BQ, TR, theta)
euler_errors = np.zeros((p.T, 2 * p.S, p.J))
lazy_values = []
for j in range(p.J):
guesses = (guesses_b[:, :, j], guesses_n[:, :, j])
lazy_values.append(
delayed(inner_loop)(guesses, outer_loop_vars, initial_values,
j, ind, p))
results = compute(*lazy_values,
scheduler=dask.multiprocessing.get,
num_workers=p.num_workers)
for j, result in enumerate(results):
euler_errors[:, :, j], b_mat[:, :, j], n_mat[:, :, j] = result
bmat_s = np.zeros((p.T, p.S, p.J))
bmat_s[0, 1:, :] = initial_b[:-1, :]
bmat_s[1:, 1:, :] = b_mat[:p.T - 1, :-1, :]
bmat_splus1 = np.zeros((p.T, p.S, p.J))
bmat_splus1[:, :, :] = b_mat[:p.T, :, :]
etr_params_4D = np.tile(
p.etr_params.reshape(p.T, p.S, 1, p.etr_params.shape[2]),
(1, 1, p.J, 1))
bqmat = household.get_bq(BQ, None, p, 'TPI')
trmat = household.get_tr(TR, None, p, 'TPI')
tax_mat = tax.total_taxes(r_hh[:p.T], w[:p.T], bmat_s,
n_mat[:p.T, :, :], bqmat[:p.T, :, :], factor,
trmat[:p.T, :, :], theta, 0, None, False,
'TPI', p.e, etr_params_4D, p)
r_hh_path = utils.to_timepath_shape(r_hh, p)
wpath = utils.to_timepath_shape(w, p)
c_mat = household.get_cons(r_hh_path[:p.T, :, :], wpath[:p.T, :, :],
bmat_s, bmat_splus1, n_mat[:p.T, :, :],
bqmat[:p.T, :, :], tax_mat, p.e,
p.tau_c[:p.T, :, :], p)
y_before_tax_mat = (r_hh_path[:p.T, :, :] * bmat_s[:p.T, :, :] +
wpath[:p.T, :, :] * p.e * n_mat[:p.T, :, :])
if not p.baseline_spending and not p.budget_balance:
Y[:p.T] = TR[:p.T] / p.alpha_T[:p.T] # maybe unecessary
(total_rev, T_Ipath, T_Ppath, T_BQpath,
T_Wpath, T_Cpath, business_revenue) = aggr.revenue(
r_hh[:p.T], w[:p.T], bmat_s, n_mat[:p.T, :, :],
bqmat[:p.T, :, :], c_mat[:p.T, :, :], Y[:p.T], L[:p.T],
K[:p.T], factor, theta, etr_params_4D, p, 'TPI')
total_revenue[:p.T] = total_rev
# set intial debt value
if p.baseline:
D0 = p.initial_debt_ratio * Y[0]
if not p.baseline_spending:
G_0 = p.alpha_G[0] * Y[0]
dg_fixed_values = (Y, total_revenue, TR, D0, G_0)
Dnew, G[:p.T] = fiscal.D_G_path(r_gov, dg_fixed_values, Gbaseline,
p)
# Fix initial amount of foreign debt holding
D_f[0] = p.initial_foreign_debt_ratio * Dnew[0]
for t in range(1, p.T):
D_f[t + 1] = (D_f[t] / (np.exp(p.g_y) * (1 + p.g_n[t + 1])) +
p.zeta_D[t] * (Dnew[t + 1] -
(Dnew[t] / (np.exp(p.g_y) *
(1 + p.g_n[t + 1])))))
D_d[:p.T] = Dnew[:p.T] - D_f[:p.T]
else: # if budget balance
Dnew = np.zeros(p.T + 1)
G[:p.T] = np.zeros(p.T)
D_f[:p.T] = np.zeros(p.T)
D_d[:p.T] = np.zeros(p.T)
L[:p.T] = aggr.get_L(n_mat[:p.T], p, 'TPI')
B[1:p.T] = aggr.get_B(bmat_splus1[:p.T], p, 'TPI', False)[:p.T - 1]
K_demand_open = firm.get_K(L[:p.T], p.firm_r[:p.T], p, 'TPI')
K_d[:p.T] = B[:p.T] - D_d[:p.T]
if np.any(K_d < 0):
print('K_d has negative elements. Setting them ' +
'positive to prevent NAN.')
K_d[:p.T] = np.fmax(K_d[:p.T], 0.05 * B[:p.T])
K_f[:p.T] = p.zeta_K[:p.T] * (K_demand_open - B[:p.T] + D_d[:p.T])
K = K_f + K_d
if np.any(B) < 0:
print('B has negative elements. B[0:9]:', B[0:9])
print('B[T-2:T]:', B[p.T - 2, p.T])
if p.small_open:
K[:p.T] = K_demand_open
Ynew = firm.get_Y(K[:p.T], L[:p.T], p, 'TPI')
rnew = r.copy()
if not p.small_open:
rnew[:p.T] = firm.get_r(Ynew[:p.T], K[:p.T], p, 'TPI')
else:
rnew[:p.T] = r[:p.T].copy()
r_gov_new = fiscal.get_r_gov(rnew, p)
if p.budget_balance:
r_hh_new = rnew[:p.T]
else:
r_hh_new = aggr.get_r_hh(rnew[:p.T], r_gov_new[:p.T], K[:p.T],
Dnew[:p.T])
if p.small_open:
r_hh_new = p.hh_r[:p.T]
# compute w
wnew = firm.get_w_from_r(rnew[:p.T], p, 'TPI')
b_mat_shift = np.append(np.reshape(initial_b, (1, p.S, p.J)),
b_mat[:p.T - 1, :, :],
axis=0)
BQnew = aggr.get_BQ(r_hh_new[:p.T], b_mat_shift, None, p, 'TPI', False)
bqmat_new = household.get_bq(BQnew, None, p, 'TPI')
(total_rev, T_Ipath, T_Ppath, T_BQpath,
T_Wpath, T_Cpath, business_revenue) = aggr.revenue(
r_hh_new[:p.T], wnew[:p.T], bmat_s, n_mat[:p.T, :, :],
bqmat_new[:p.T, :, :], c_mat[:p.T, :, :], Ynew[:p.T], L[:p.T],
K[:p.T], factor, theta, etr_params_4D, p, 'TPI')
total_revenue[:p.T] = total_rev
if p.budget_balance:
TR_new = total_revenue
elif not p.baseline_spending:
TR_new = p.alpha_T[:p.T] * Ynew[:p.T]
# If baseline_spending==True, no need to update TR, it's fixed
# update vars for next iteration
w[:p.T] = wnew[:p.T]
r[:p.T] = utils.convex_combo(rnew[:p.T], r[:p.T], p.nu)
BQ[:p.T] = utils.convex_combo(BQnew[:p.T], BQ[:p.T], p.nu)
D[:p.T] = Dnew[:p.T]
Y[:p.T] = utils.convex_combo(Ynew[:p.T], Y[:p.T], p.nu)
if not p.baseline_spending:
TR[:p.T] = utils.convex_combo(TR_new[:p.T], TR[:p.T], p.nu)
guesses_b = utils.convex_combo(b_mat, guesses_b, p.nu)
guesses_n = utils.convex_combo(n_mat, guesses_n, p.nu)
print('r diff: ', (rnew[:p.T] - r[:p.T]).max(),
(rnew[:p.T] - r[:p.T]).min())
print('BQ diff: ', (BQnew[:p.T] - BQ[:p.T]).max(),
(BQnew[:p.T] - BQ[:p.T]).min())
print('TR diff: ', (TR_new[:p.T] - TR[:p.T]).max(),
(TR_new[:p.T] - TR[:p.T]).min())
print('Y diff: ', (Ynew[:p.T] - Y[:p.T]).max(),
(Ynew[:p.T] - Y[:p.T]).min())
if not p.baseline_spending:
if TR.all() != 0:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) + list(
utils.pct_diff_func(BQnew[:p.T], BQ[:p.T]).flatten()) +
list(utils.pct_diff_func(TR_new[:p.T], TR[:p.T]))).max()
else:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) + list(
utils.pct_diff_func(BQnew[:p.T], BQ[:p.T]).flatten()) +
list(np.abs(TR[:p.T]))).max()
else:
TPIdist = np.array(
list(utils.pct_diff_func(rnew[:p.T], r[:p.T])) +
list(utils.pct_diff_func(BQnew[:p.T], BQ[:p.T]).flatten()) +
list(utils.pct_diff_func(Ynew[:p.T], Y[:p.T]))).max()
TPIdist_vec[TPIiter] = TPIdist
# After T=10, if cycling occurs, drop the value of nu
# wait til after T=10 or so, because sometimes there is a jump up
# in the first couple iterations
# if TPIiter > 10:
# if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0:
# nu /= 2
# print 'New Value of nu:', nu
TPIiter += 1
print('Iteration:', TPIiter)
print('\tDistance:', TPIdist)
# Compute effective and marginal tax rates for all agents
mtrx_params_4D = np.tile(
p.mtrx_params.reshape(p.T, p.S, 1, p.mtrx_params.shape[2]),
(1, 1, p.J, 1))
mtry_params_4D = np.tile(
p.mtry_params.reshape(p.T, p.S, 1, p.mtry_params.shape[2]),
(1, 1, p.J, 1))
e_3D = np.tile(p.e.reshape(1, p.S, p.J), (p.T, 1, 1))
mtry_path = tax.MTR_income(r_hh_path[:p.T], wpath[:p.T],
bmat_s[:p.T, :, :], n_mat[:p.T, :, :], factor,
True, e_3D, etr_params_4D, mtry_params_4D, p)
mtrx_path = tax.MTR_income(r_hh_path[:p.T], wpath[:p.T],
bmat_s[:p.T, :, :], n_mat[:p.T, :, :], factor,
False, e_3D, etr_params_4D, mtrx_params_4D, p)
etr_path = tax.ETR_income(r_hh_path[:p.T], wpath[:p.T], bmat_s[:p.T, :, :],
n_mat[:p.T, :, :], factor, e_3D, etr_params_4D,
p)
C = aggr.get_C(c_mat, p, 'TPI')
# Note that implicity in this computation is that immigrants'
# wealth is all in the form of private capital
I_d = aggr.get_I(bmat_splus1[:p.T], K_d[1:p.T + 1], K_d[:p.T], p, 'TPI')
I = aggr.get_I(bmat_splus1[:p.T], K[1:p.T + 1], K[:p.T], p, 'TPI')
# solve resource constraint
# net foreign borrowing
new_borrowing_f = (D_f[1:p.T + 1] * np.exp(p.g_y) *
(1 + p.g_n[1:p.T + 1]) - D_f[:p.T])
debt_service_f = D_f * r_hh
RC_error = aggr.resource_constraint(Y[:p.T - 1], C[:p.T - 1], G[:p.T - 1],
I_d[:p.T - 1], K_f[:p.T - 1],
new_borrowing_f[:p.T - 1],
debt_service_f[:p.T - 1],
r_hh[:p.T - 1], p)
# Compute total investment (not just domestic)
I_total = ((1 + p.g_n[:p.T]) * np.exp(p.g_y) * K[1:p.T + 1] -
(1.0 - p.delta) * K[:p.T])
rce_max = np.amax(np.abs(RC_error))
print('Max absolute value resource constraint error:', rce_max)
print('Checking time path for violations of constraints.')
for t in range(p.T):
household.constraint_checker_TPI(b_mat[t], n_mat[t], c_mat[t], t,
p.ltilde)
eul_savings = euler_errors[:, :p.S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, p.S:, :].max(1).max(1)
print('Max Euler error, savings: ', eul_savings)
print('Max Euler error labor supply: ', eul_laborleisure)
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {
'Y': Y[:p.T],
'B': B,
'K': K,
'K_f': K_f,
'K_d': K_d,
'L': L,
'C': C,
'I': I,
'I_total': I_total,
'I_d': I_d,
'BQ': BQ,
'total_revenue': total_revenue,
'business_revenue': business_revenue,
'IITpayroll_revenue': T_Ipath,
'TR': TR,
'T_P': T_Ppath,
'T_BQ': T_BQpath,
'T_W': T_Wpath,
'T_C': T_Cpath,
'G': G,
'D': D,
'D_f': D_f,
'D_d': D_d,
'r': r,
'r_gov': r_gov,
'r_hh': r_hh,
'w': w,
'bmat_splus1': bmat_splus1,
'bmat_s': bmat_s[:p.T, :, :],
'n_mat': n_mat[:p.T, :, :],
'c_path': c_mat,
'bq_path': bqmat,
'tr_path': trmat,
'y_before_tax_mat': y_before_tax_mat,
'tax_path': tax_mat,
'eul_savings': eul_savings,
'eul_laborleisure': eul_laborleisure,
'resource_constraint_error': RC_error,
'new_borrowing_f': new_borrowing_f,
'debt_service_f': debt_service_f,
'etr_path': etr_path,
'mtrx_path': mtrx_path,
'mtry_path': mtry_path
}
tpi_dir = os.path.join(p.output_base, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(output, open(tpi_vars, "wb"))
if np.any(G) < 0:
print('Government spending is negative along transition path' +
' to satisfy budget')
if (((TPIiter >= p.maxiter) or (np.absolute(TPIdist) > p.mindist_TPI))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError('Transition path equlibrium not found' +
' (TPIdist)')
if ((np.any(np.absolute(RC_error) >= p.mindist_TPI * 10))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError('Transition path equlibrium not found ' +
'(RC_error)')
if ((np.any(np.absolute(eul_savings) >= p.mindist_TPI) or
(np.any(np.absolute(eul_laborleisure) > p.mindist_TPI)))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError('Transition path equlibrium not found ' +
'(eulers)')
return output
def inner_loop(guesses, outer_loop_vars, initial_values, j, ind, p):
'''
Given path of economic aggregates and factor prices, solves
household problem. This has been termed the inner-loop (in
constrast to the outer fixed point loop that soves for GE factor
prices and economic aggregates).
Args:
guesses (tuple): initial guesses for b and n, (guesses_b,
guesses_n)
outer_loop_vars (tuple): values for factor prices and economic
aggregates used in household problem (r, w, r_hh, BQ, TR,
theta)
r (Numpy array): real interest rate on private capital
w (Numpy array): real wage rate
r (Numpy array): real interest rate on household portfolio
BQ (array_like): aggregate bequest amounts
TR (Numpy array): lump sum transfer amount
theta (Numpy array): retirement replacement rates, length J
initial_values (tuple): initial period variable values,
(b_sinit, b_splus1init, factor, initial_b, initial_n, D0)
j (int): index of ability type
ind (Numpy array): integers from 0 to S-1
p (OG-India Specifcations object): model parameters
Returns:
euler_errors (Numpy array): errors from FOCs, size = Tx2S
b_mat (Numpy array): savings amounts, size = TxS
n_mat (Numpy array): labor supply amounts, size = TxS
'''
# unpack variables and parameters pass to function
(K0, b_sinit, b_splus1init, factor, initial_b, initial_n,
D0) = initial_values
guesses_b, guesses_n = guesses
r, w, r_hh, BQ, TR, theta = outer_loop_vars
# compute w
w[:p.T] = firm.get_w_from_r(r[:p.T], p, 'TPI')
# compute bq
bq = household.get_bq(BQ, None, p, 'TPI')
# compute tr
tr = household.get_tr(TR, None, p, 'TPI')
# initialize arrays
b_mat = np.zeros((p.T + p.S, p.S))
n_mat = np.zeros((p.T + p.S, p.S))
euler_errors = np.zeros((p.T, 2 * p.S))
b_mat[0, -1], n_mat[0, -1] =\
np.array(opt.fsolve(firstdoughnutring, [guesses_b[0, -1],
guesses_n[0, -1]],
args=(r_hh[0], w[0], bq[0, -1, j],
tr[0, -1, j],
theta * p.replacement_rate_adjust[0],
factor, j, initial_b, p),
xtol=MINIMIZER_TOL))
for s in range(p.S - 2): # Upper triangle
ind2 = np.arange(s + 2)
b_guesses_to_use = np.diag(guesses_b[:p.S, :], p.S - (s + 2))
n_guesses_to_use = np.diag(guesses_n[:p.S, :], p.S - (s + 2))
theta_to_use = theta[j] * p.replacement_rate_adjust[:p.S]
bq_to_use = np.diag(bq[:p.S, :, j], p.S - (s + 2))
tr_to_use = np.diag(tr[:p.S, :, j], p.S - (s + 2))
tau_c_to_use = np.diag(p.tau_c[:p.S, :, j], p.S - (s + 2))
length_diag =\
np.diag(p.etr_params[:p.S, :, 0], p.S-(s + 2)).shape[0]
etr_params_to_use = np.zeros((length_diag, p.etr_params.shape[2]))
mtrx_params_to_use = np.zeros((length_diag, p.mtrx_params.shape[2]))
mtry_params_to_use = np.zeros((length_diag, p.mtry_params.shape[2]))
for i in range(p.etr_params.shape[2]):
etr_params_to_use[:, i] =\
np.diag(p.etr_params[:p.S, :, i], p.S - (s + 2))
mtrx_params_to_use[:, i] =\
np.diag(p.mtrx_params[:p.S, :, i], p.S - (s + 2))
mtry_params_to_use[:, i] =\
np.diag(p.mtry_params[:p.S, :, i], p.S - (s + 2))
solutions = opt.fsolve(
twist_doughnut,
list(b_guesses_to_use) + list(n_guesses_to_use),
args=(r_hh, w, bq_to_use, tr_to_use, theta_to_use, factor, j, s, 0,
tau_c_to_use, etr_params_to_use, mtrx_params_to_use,
mtry_params_to_use, initial_b, p),
xtol=MINIMIZER_TOL)
b_vec = solutions[:int(len(solutions) / 2)]
b_mat[ind2, p.S - (s + 2) + ind2] = b_vec
n_vec = solutions[int(len(solutions) / 2):]
n_mat[ind2, p.S - (s + 2) + ind2] = n_vec
for t in range(0, p.T):
b_guesses_to_use = .75 * \
np.diag(guesses_b[t:t + p.S, :])
n_guesses_to_use = np.diag(guesses_n[t:t + p.S, :])
theta_to_use = theta[j] * p.replacement_rate_adjust[t:t + p.S]
bq_to_use = np.diag(bq[t:t + p.S, :, j])
tr_to_use = np.diag(tr[t:t + p.S, :, j])
tau_c_to_use = np.diag(p.tau_c[t:t + p.S, :, j])
# initialize array of diagonal elements
etr_params_TP = np.zeros((p.T + p.S, p.S, p.etr_params.shape[2]))
etr_params_TP[:p.T, :, :] = p.etr_params
etr_params_TP[p.T:, :, :] = p.etr_params[-1, :, :]
mtrx_params_TP = np.zeros((p.T + p.S, p.S, p.mtrx_params.shape[2]))
mtrx_params_TP[:p.T, :, :] = p.mtrx_params
mtrx_params_TP[p.T:, :, :] = p.mtrx_params[-1, :, :]
mtry_params_TP = np.zeros((p.T + p.S, p.S, p.mtry_params.shape[2]))
mtry_params_TP[:p.T, :, :] = p.mtry_params
mtry_params_TP[p.T:, :, :] = p.mtry_params[-1, :, :]
length_diag =\
np.diag(etr_params_TP[t:t + p.S, :, 0]).shape[0]
etr_params_to_use = np.zeros((length_diag, p.etr_params.shape[2]))
mtrx_params_to_use = np.zeros((length_diag, p.mtrx_params.shape[2]))
mtry_params_to_use = np.zeros((length_diag, p.mtry_params.shape[2]))
for i in range(p.etr_params.shape[2]):
etr_params_to_use[:, i] = np.diag(etr_params_TP[t:t + p.S, :, i])
mtrx_params_to_use[:, i] = np.diag(mtrx_params_TP[t:t + p.S, :, i])
mtry_params_to_use[:, i] = np.diag(mtry_params_TP[t:t + p.S, :, i])
#
# TPI_solver_params = (inc_tax_params_TP, tpi_params, None)
[solutions, infodict, ier, message] =\
opt.fsolve(twist_doughnut, list(b_guesses_to_use) +
list(n_guesses_to_use),
args=(r_hh, w, bq_to_use, tr_to_use,
theta_to_use, factor,
j, None, t, tau_c_to_use,
etr_params_to_use, mtrx_params_to_use,
mtry_params_to_use, initial_b, p),
xtol=MINIMIZER_TOL, full_output=True)
euler_errors[t, :] = infodict['fvec']
b_vec = solutions[:p.S]
b_mat[t + ind, ind] = b_vec
n_vec = solutions[p.S:]
n_mat[t + ind, ind] = n_vec
print('Type ', j, ' max euler error = ', euler_errors.max())
return euler_errors, b_mat, n_mat
def inner_loop(outer_loop_vars, p, client):
'''
This function solves for the inner loop of the SS. That is, given
the guesses of the outer loop variables (r, w, TR, factor) this
function solves the households' problems in the SS.
Args:
outer_loop_vars (tuple): tuple of outer loop variables,
(bssmat, nssmat, r, BQ, TR, factor) or
(bssmat, nssmat, r, BQ, Y, TR, factor)
bssmat (Numpy array): initial guess at savings, size = SxJ
nssmat (Numpy array): initial guess at labor supply, size = SxJ
BQ (array_like): aggregate bequest amount(s)
Y (scalar): real GDP
TR (scalar): lump sum transfer amount
factor (scalar): scaling factor converting model units to dollars
w (scalar): real wage rate
p (OG-India Specifcations object): model parameters
client (Dask client object): client
Returns:
euler_errors (Numpy array): errors terms from FOCs, size = 2SxJ
bssmat (Numpy array): savings, size = SxJ
nssmat (Numpy array): labor supply, size = SxJ
new_r (scalar): real interest rate on firm capital
new_r_gov (scalar): real interest rate on government debt
new_r_hh (scalar): real interest rate on household portfolio
new_w (scalar): real wage rate
new_TR (scalar): lump sum transfer amount
new_Y (scalar): real GDP
new_factor (scalar): scaling factor converting model units to
dollars
new_BQ (array_like): aggregate bequest amount(s)
average_income_model (scalar): average income in model units
'''
# unpack variables to pass to function
if p.budget_balance:
bssmat, nssmat, r, BQ, TR, factor = outer_loop_vars
else:
bssmat, nssmat, r, BQ, Y, TR, factor = outer_loop_vars
euler_errors = np.zeros((2 * p.S, p.J))
w = firm.get_w_from_r(r, p, 'SS')
r_gov = fiscal.get_r_gov(r, p)
if p.budget_balance:
r_hh = r
D = 0
else:
D = p.debt_ratio_ss * Y
K = firm.get_K_from_Y(Y, r, p, 'SS')
r_hh = aggr.get_r_hh(r, r_gov, K, D)
if p.small_open:
r_hh = p.hh_r[-1]
bq = household.get_bq(BQ, None, p, 'SS')
tr = household.get_tr(TR, None, p, 'SS')
lazy_values = []
for j in range(p.J):
guesses = np.append(bssmat[:, j], nssmat[:, j])
euler_params = (r_hh, w, bq[:, j], tr[:, j], factor, j, p)
lazy_values.append(
delayed(opt.fsolve)(euler_equation_solver,
guesses * .9,
args=euler_params,
xtol=MINIMIZER_TOL,
full_output=True))
results = compute(*lazy_values,
scheduler=dask.multiprocessing.get,
num_workers=p.num_workers)
# for j, result in results.items():
for j, result in enumerate(results):
[solutions, infodict, ier, message] = result
euler_errors[:, j] = infodict['fvec']
bssmat[:, j] = solutions[:p.S]
nssmat[:, j] = solutions[p.S:]
L = aggr.get_L(nssmat, p, 'SS')
B = aggr.get_B(bssmat, p, 'SS', False)
K_demand_open = firm.get_K(L, p.firm_r[-1], p, 'SS')
D_f = p.zeta_D[-1] * D
D_d = D - D_f
if not p.small_open:
K_d = B - D_d
K_f = p.zeta_K[-1] * (K_demand_open - B + D_d)
K = K_f + K_d
else:
# can remove this else statement by making small open the case
# where zeta_K = 1
K_d = B - D_d
K_f = K_demand_open - B + D_d
K = K_f + K_d
new_Y = firm.get_Y(K, L, p, 'SS')
if p.budget_balance:
Y = new_Y
if not p.small_open:
new_r = firm.get_r(Y, K, p, 'SS')
else:
new_r = p.firm_r[-1]
new_w = firm.get_w_from_r(new_r, p, 'SS')
b_s = np.array(list(np.zeros(p.J).reshape(1, p.J)) + list(bssmat[:-1, :]))
new_r_gov = fiscal.get_r_gov(new_r, p)
new_r_hh = aggr.get_r_hh(new_r, new_r_gov, K, D)
average_income_model = ((new_r_hh * b_s + new_w * p.e * nssmat) *
p.omega_SS.reshape(p.S, 1) *
p.lambdas.reshape(1, p.J)).sum()
if p.baseline:
new_factor = p.mean_income_data / average_income_model
else:
new_factor = factor
new_BQ = aggr.get_BQ(new_r_hh, bssmat, None, p, 'SS', False)
new_bq = household.get_bq(new_BQ, None, p, 'SS')
tr = household.get_tr(TR, None, p, 'SS')
theta = tax.replacement_rate_vals(nssmat, new_w, new_factor, None, p)
if p.budget_balance:
etr_params_3D = np.tile(
np.reshape(p.etr_params[-1, :, :],
(p.S, 1, p.etr_params.shape[2])), (1, p.J, 1))
taxss = tax.total_taxes(new_r_hh, new_w, b_s, nssmat, new_bq, factor,
tr, theta, None, None, False, 'SS', p.e,
etr_params_3D, p)
cssmat = household.get_cons(new_r_hh, new_w, b_s, bssmat, nssmat,
new_bq, taxss, p.e, p.tau_c[-1, :, :], p)
new_TR, _, _, _, _, _, _ = aggr.revenue(new_r_hh, new_w, b_s, nssmat,
new_bq, cssmat, new_Y, L, K,
factor, theta, etr_params_3D,
p, 'SS')
elif p.baseline_spending:
new_TR = TR
else:
new_TR = p.alpha_T[-1] * new_Y
return euler_errors, bssmat, nssmat, new_r, new_r_gov, new_r_hh, \
new_w, new_TR, new_Y, new_factor, new_BQ, average_income_model
def SS_solver(bmat, nmat, r, BQ, TR, factor, Y, p, client, fsolve_flag=False):
'''
Solves for the steady state distribution of capital, labor, as well
as w, r, TR and the scaling factor, using functional iteration.
Args:
bmat (Numpy array): initial guess at savings, size = SxJ
nmat (Numpy array): initial guess at labor supply, size = SxJ
r (scalar): real interest rate
BQ (array_like): aggregate bequest amount(s)
TR (scalar): lump sum transfer amount
factor (scalar): scaling factor converting model units to dollars
Y (scalar): real GDP
p (OG-India Specifcations object): model parameters
client (Dask client object): client
Returns:
output (dictionary): dictionary with steady state solution
results
'''
# Rename the inputs
if not p.budget_balance:
if not p.baseline_spending:
Y = TR / p.alpha_T[-1]
if p.small_open:
r = p.hh_r[-1]
dist = 10
iteration = 0
dist_vec = np.zeros(p.maxiter)
maxiter_ss = p.maxiter
nu_ss = p.nu
if fsolve_flag:
maxiter_ss = 1
while (dist > p.mindist_SS) and (iteration < maxiter_ss):
# Solve for the steady state levels of b and n, given w, r,
# Y and factor
if p.budget_balance:
outer_loop_vars = (bmat, nmat, r, BQ, TR, factor)
else:
outer_loop_vars = (bmat, nmat, r, BQ, Y, TR, factor)
(euler_errors, new_bmat, new_nmat, new_r, new_r_gov, new_r_hh,
new_w, new_TR, new_Y, new_factor, new_BQ,
average_income_model) =\
inner_loop(outer_loop_vars, p, client)
r = utils.convex_combo(new_r, r, nu_ss)
factor = utils.convex_combo(new_factor, factor, nu_ss)
BQ = utils.convex_combo(new_BQ, BQ, nu_ss)
# bmat = utils.convex_combo(new_bmat, bmat, nu_ss)
# nmat = utils.convex_combo(new_nmat, nmat, nu_ss)
if not p.baseline_spending:
TR = utils.convex_combo(new_TR, TR, nu_ss)
dist = np.array([utils.pct_diff_func(new_r, r)] +
list(utils.pct_diff_func(new_BQ, BQ)) +
[utils.pct_diff_func(new_TR, TR)] +
[utils.pct_diff_func(new_factor, factor)]).max()
else:
Y = utils.convex_combo(new_Y, Y, nu_ss)
if Y != 0:
dist = np.array(
[utils.pct_diff_func(new_r, r)] +
list(utils.pct_diff_func(new_BQ, BQ)) +
[utils.pct_diff_func(new_Y, Y)] +
[utils.pct_diff_func(new_factor, factor)]).max()
else:
# If Y is zero (if there is no output), a percent difference
# will throw NaN's, so we use an absolute difference
dist = np.array(
[utils.pct_diff_func(new_r, r)] +
list(utils.pct_diff_func(new_BQ, BQ)) + [abs(new_Y - Y)] +
[utils.pct_diff_func(new_factor, factor)]).max()
dist_vec[iteration] = dist
# Similar to TPI: if the distance between iterations increases, then
# decrease the value of nu to prevent cycling
if iteration > 10:
if dist_vec[iteration] - dist_vec[iteration - 1] > 0:
nu_ss /= 2.0
print('New value of nu:', nu_ss)
iteration += 1
print('Iteration: %02d' % iteration, ' Distance: ', dist)
# Generate the SS values of variables, including euler errors
bssmat_s = np.append(np.zeros((1, p.J)), bmat[:-1, :], axis=0)
bssmat_splus1 = bmat
nssmat = nmat
rss = r
r_gov_ss = fiscal.get_r_gov(rss, p)
if p.budget_balance:
r_hh_ss = rss
Dss = 0.0
else:
Dss = p.debt_ratio_ss * Y
Lss = aggr.get_L(nssmat, p, 'SS')
Bss = aggr.get_B(bssmat_splus1, p, 'SS', False)
K_demand_open_ss = firm.get_K(Lss, p.firm_r[-1], p, 'SS')
D_f_ss = p.zeta_D[-1] * Dss
D_d_ss = Dss - D_f_ss
K_d_ss = Bss - D_d_ss
if not p.small_open:
K_f_ss = p.zeta_K[-1] * (K_demand_open_ss - Bss + D_d_ss)
Kss = K_f_ss + K_d_ss
# Note that implicity in this computation is that immigrants'
# wealth is all in the form of private capital
I_d_ss = aggr.get_I(bssmat_splus1, K_d_ss, K_d_ss, p, 'SS')
Iss = aggr.get_I(bssmat_splus1, Kss, Kss, p, 'SS')
else:
K_d_ss = Bss - D_d_ss
K_f_ss = K_demand_open_ss - Bss + D_d_ss
Kss = K_f_ss + K_d_ss
InvestmentPlaceholder = np.zeros(bssmat_splus1.shape)
Iss = aggr.get_I(InvestmentPlaceholder, Kss, Kss, p, 'SS')
I_d_ss = aggr.get_I(bssmat_splus1, K_d_ss, K_d_ss, p, 'SS')
r_hh_ss = aggr.get_r_hh(rss, r_gov_ss, Kss, Dss)
wss = new_w
BQss = new_BQ
factor_ss = factor
TR_ss = TR
bqssmat = household.get_bq(BQss, None, p, 'SS')
trssmat = household.get_tr(TR_ss, None, p, 'SS')
Yss = firm.get_Y(Kss, Lss, p, 'SS')
theta = tax.replacement_rate_vals(nssmat, wss, factor_ss, None, p)
# Compute effective and marginal tax rates for all agents
etr_params_3D = np.tile(
np.reshape(p.etr_params[-1, :, :], (p.S, 1, p.etr_params.shape[2])),
(1, p.J, 1))
mtrx_params_3D = np.tile(
np.reshape(p.mtrx_params[-1, :, :], (p.S, 1, p.mtrx_params.shape[2])),
(1, p.J, 1))
mtry_params_3D = np.tile(
np.reshape(p.mtry_params[-1, :, :], (p.S, 1, p.mtry_params.shape[2])),
(1, p.J, 1))
mtry_ss = tax.MTR_income(r_hh_ss, wss, bssmat_s, nssmat, factor, True, p.e,
etr_params_3D, mtry_params_3D, p)
mtrx_ss = tax.MTR_income(r_hh_ss, wss, bssmat_s, nssmat, factor, False,
p.e, etr_params_3D, mtrx_params_3D, p)
etr_ss = tax.ETR_income(r_hh_ss, wss, bssmat_s, nssmat, factor, p.e,
etr_params_3D, p)
taxss = tax.total_taxes(r_hh_ss, wss, bssmat_s, nssmat, bqssmat, factor_ss,
trssmat, theta, None, None, False, 'SS', p.e,
etr_params_3D, p)
cssmat = household.get_cons(r_hh_ss, wss, bssmat_s, bssmat_splus1, nssmat,
bqssmat, taxss, p.e, p.tau_c[-1, :, :], p)
yss_before_tax_mat = r_hh_ss * bssmat_s + wss * p.e * nssmat
Css = aggr.get_C(cssmat, p, 'SS')
(total_revenue_ss, T_Iss, T_Pss, T_BQss, T_Wss, T_Css,
business_revenue) =\
aggr.revenue(r_hh_ss, wss, bssmat_s, nssmat, bqssmat, cssmat,
Yss, Lss, Kss, factor, theta, etr_params_3D, p,
'SS')
debt_service_ss = r_gov_ss * Dss
new_borrowing = Dss * ((1 + p.g_n_ss) * np.exp(p.g_y) - 1)
# government spends such that it expands its debt at the same rate as GDP
if p.budget_balance:
Gss = 0.0
else:
Gss = total_revenue_ss + new_borrowing - (TR_ss + debt_service_ss)
print('G components = ', new_borrowing, TR_ss, debt_service_ss)
# Compute total investment (not just domestic)
Iss_total = ((1 + p.g_n_ss) * np.exp(p.g_y) - 1 + p.delta) * Kss
# solve resource constraint
# net foreign borrowing
print('Foreign debt holdings = ', D_f_ss)
print('Foreign capital holdings = ', K_f_ss)
new_borrowing_f = D_f_ss * (np.exp(p.g_y) * (1 + p.g_n_ss) - 1)
debt_service_f = D_f_ss * r_hh_ss
RC = aggr.resource_constraint(Yss, Css, Gss, I_d_ss, K_f_ss,
new_borrowing_f, debt_service_f, r_hh_ss, p)
print('resource constraint: ', RC)
if Gss < 0:
print('Steady state government spending is negative to satisfy' +
' budget')
if ENFORCE_SOLUTION_CHECKS and (np.absolute(RC) > p.mindist_SS):
print('Resource Constraint Difference:', RC)
err = 'Steady state aggregate resource constraint not satisfied'
raise RuntimeError(err)
# check constraints
household.constraint_checker_SS(bssmat_splus1, nssmat, cssmat, p.ltilde)
euler_savings = euler_errors[:p.S, :]
euler_labor_leisure = euler_errors[p.S:, :]
print('Maximum error in labor FOC = ',
np.absolute(euler_labor_leisure).max())
print('Maximum error in savings FOC = ', np.absolute(euler_savings).max())
# Return dictionary of SS results
output = {
'Kss': Kss,
'K_f_ss': K_f_ss,
'K_d_ss': K_d_ss,
'Bss': Bss,
'Lss': Lss,
'Css': Css,
'Iss': Iss,
'Iss_total': Iss_total,
'I_d_ss': I_d_ss,
'nssmat': nssmat,
'Yss': Yss,
'Dss': Dss,
'D_f_ss': D_f_ss,
'D_d_ss': D_d_ss,
'wss': wss,
'rss': rss,
'r_gov_ss': r_gov_ss,
'r_hh_ss': r_hh_ss,
'theta': theta,
'BQss': BQss,
'factor_ss': factor_ss,
'bssmat_s': bssmat_s,
'cssmat': cssmat,
'bssmat_splus1': bssmat_splus1,
'yss_before_tax_mat': yss_before_tax_mat,
'bqssmat': bqssmat,
'TR_ss': TR_ss,
'trssmat': trssmat,
'Gss': Gss,
'total_revenue_ss': total_revenue_ss,
'business_revenue': business_revenue,
'IITpayroll_revenue': T_Iss,
'T_Pss': T_Pss,
'T_BQss': T_BQss,
'T_Wss': T_Wss,
'T_Css': T_Css,
'euler_savings': euler_savings,
'debt_service_f': debt_service_f,
'new_borrowing_f': new_borrowing_f,
'debt_service_ss': debt_service_ss,
'new_borrowing': new_borrowing,
'euler_labor_leisure': euler_labor_leisure,
'resource_constraint_error': RC,
'etr_ss': etr_ss,
'mtrx_ss': mtrx_ss,
'mtry_ss': mtry_ss
}
return output