def test_D_G_path(baseline_spending, Y, TR, Revenue, Gbaseline, budget_balance,
expected_tuple):
p = Specifications()
new_param_values = {
'T': 320,
'S': 80,
'debt_ratio_ss': 1.2,
'tG1': 20,
'tG2': 256,
'alpha_T': [0.09],
'alpha_G': [0.05],
'rho_G': 0.1,
'g_y_annual': 0.03,
'baseline_spending': baseline_spending,
'budget_balance': budget_balance
}
p.update_specifications(new_param_values, raise_errors=False)
r_gov = np.ones(p.T + p.S) * 0.03
p.g_n = np.ones(p.T + p.S) * 0.02
D0_baseline = 0.59
Gbaseline[0] = 0.05
dg_fixed_values = (Y, Revenue, TR, Gbaseline, D0_baseline)
test_tuple = fiscal.D_G_path(r_gov, dg_fixed_values, p)
for i, v in enumerate(test_tuple):
assert np.allclose(v[:p.T], expected_tuple[i][:p.T])
def test_D_G_path(baseline_spending, Y, T_H, REVENUE, Gbaseline,
D_expected, G_expected):
p = Specifications()
new_param_values = {
'T': 320,
'S': 80,
'debt_ratio_ss': 1.2,
'tG1': 20,
'tG2': 256,
'alpha_T': [0.09],
'alpha_G': [0.05],
'rho_G': 0.1,
'g_y_annual': 0.03,
'budget_balance': False,
'baseline_spending': baseline_spending
}
p.update_specifications(new_param_values, raise_errors=False)
r_gov = np.ones(p.T + p.S) * 0.03
p.g_n = np.ones(p.T + p.S) * 0.02
D0 = 0.59
G0 = 0.05
dg_fixed_values = (Y, REVENUE, T_H, D0, G0)
test_D, test_G = fiscal.D_G_path(r_gov, dg_fixed_values, Gbaseline,
p)
assert np.allclose(test_D, D_expected)
assert np.allclose(test_G, G_expected)
def run_TPI(p, client=None):
'''
Solve for transition path equilibrium of OG-USA.
Args:
p (OG-USA Specifications 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) =\
initial_values
(TRbaseline, Gbaseline, D0_baseline) = 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
guesses_b = utils.get_initial_path(initial_b, ss_vars['bssmat_splus1'], p,
'ratio')
guesses_n = utils.get_initial_path(initial_n, ss_vars['nssmat'], p,
'ratio')
b_mat = guesses_b
n_mat = guesses_n
ind = np.arange(p.S)
# Get path for aggregate savings and labor supply`
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
K_init = B_init * ss_vars['Kss'] / ss_vars['Bss']
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)
r[:p.T] = firm.get_r(Y[:p.T], K[:p.T], p, 'TPI')
r[p.T:] = ss_vars['rss']
# For case where economy is small open econ
r[p.zeta_K == 1] = p.world_int_rate[p.zeta_K == 1]
# Compute other interest rates
r_gov = fiscal.get_r_gov(r, p)
r_hh = aggr.get_r_hh(r, r_gov, K, ss_vars['Dss'])
# 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']
# initial guesses at fiscal vars
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 pct changes below
else:
TR_ss2 = ss_vars['TR_ss']
TR = np.ones(p.T + p.S) * TR_ss2
total_tax_revenue = TR - ss_vars['agg_pension_outlays']
G = np.zeros(p.T + p.S)
D = np.zeros(p.T + p.S)
D_d = np.zeros(p.T + p.S)
D_f = np.zeros(p.T + p.S)
else:
if p.baseline_spending:
TR = TRbaseline
G = Gbaseline
G[p.T:] = ss_vars['Gss']
else:
TR = p.alpha_T * Y
G = np.ones(p.T + p.S) * ss_vars['Gss']
D = np.ones(p.T + p.S) * ss_vars['Dss']
D_d = D * ss_vars['D_d_ss'] / ss_vars['Dss']
D_f = D * ss_vars['D_f_ss'] / ss_vars['Dss']
total_tax_revenue = np.ones(p.T + p.S) * ss_vars['total_tax_revenue']
# Initialize bequests
BQ0 = aggr.get_BQ(r_hh[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)
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 not p.budget_balance:
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])
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))
if client:
futures = client.compute(lazy_values, num_workers=p.num_workers)
results = client.gather(futures)
else:
results = 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.net_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)
wpath = utils.to_timepath_shape(w)
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 = household.get_y(r_hh_path[:p.T, :, :],
wpath[:p.T, :, :],
bmat_s[:p.T, :, :],
n_mat[:p.T, :, :], p)
(total_tax_rev, iit_payroll_tax_revenue, agg_pension_outlays,
bequest_tax_revenue, wealth_tax_revenue, cons_tax_revenue,
business_tax_revenue, payroll_tax_revenue,
iit_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_tax_revenue[:p.T] = total_tax_rev
dg_fixed_values = (Y, total_tax_revenue, agg_pension_outlays, TR,
Gbaseline, D0_baseline)
(Dnew, G[:p.T], D_d[:p.T], D_f[:p.T], new_borrowing,
debt_service, new_borrowing_f) =\
fiscal.D_G_path(r_gov, dg_fixed_values, p)
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.world_int_rate[:p.T], p, 'TPI')
K[:p.T], K_d[:p.T], K_f[:p.T] = aggr.get_K_splits(
B[:p.T], K_demand_open, D_d[:p.T], p.zeta_K[:p.T])
Ynew = firm.get_Y(K[:p.T], L[:p.T], p, 'TPI')
rnew = r.copy()
rnew[:p.T] = firm.get_r(Ynew[:p.T], K[:p.T], p, 'TPI')
# For case where economy is small open econ
r[p.zeta_K == 1] = p.world_int_rate[p.zeta_K == 1]
r_gov_new = fiscal.get_r_gov(rnew, p)
r_hh_new = aggr.get_r_hh(rnew[:p.T], r_gov_new[:p.T], K[:p.T],
Dnew[: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_tax_rev, iit_payroll_tax_revenue, agg_pension_outlays,
bequest_tax_revenue, wealth_tax_revenue, cons_tax_revenue,
business_tax_revenue, payroll_tax_revenue,
iit_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_tax_revenue[:p.T] = total_tax_rev
TR_new = fiscal.get_TR(Ynew[:p.T], TR[:p.T], G[:p.T],
total_tax_revenue[:p.T],
agg_pension_outlays[:p.T], p, 'TPI')
# 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
# foreign debt service costs
debt_service_f = fiscal.get_debt_service_f(r_hh, D_f)
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 = aggr.get_I(None, K[1:p.T + 1], K[:p.T], p, 'total_tpi')
# Compute resource constraint error
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_tax_revenue': total_tax_revenue,
'business_tax_revenue': business_tax_revenue,
'iit_payroll_tax_revenue': iit_payroll_tax_revenue,
'iit_revenue': iit_revenue,
'payroll_tax_revenue': payroll_tax_revenue,
'TR': TR,
'agg_pension_outlays': agg_pension_outlays,
'bequest_tax_revenue': bequest_tax_revenue,
'wealth_tax_revenue': wealth_tax_revenue,
'cons_tax_revenue': cons_tax_revenue,
'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")
with open(tpi_vars, "wb") as f:
pickle.dump(output, f)
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 run_TPI(p, client=None):
# unpack tuples of parameters
initial_values, SS_values, baseline_values = get_initial_SS_values(p)
(B0, b_sinit, b_splus1init, factor, initial_b, initial_n,
D0) = initial_values
(Kss, Bss, Lss, rss, wss, BQss, T_Hss, total_revenue_ss, bssmat_splus1,
nssmat, Yss, Gss, theta) = SS_values
(T_Hbaseline, 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 = 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 * (nssmat - initial_n) + initial_n
ending_n_tail = np.tile(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 # np.zeros((p.T + p.S, p.S, p.J))
n_mat = guesses_n # np.zeros((p.T + p.S, p.S, p.J))
ind = np.arange(p.S)
L_init = np.ones((p.T + p.S, )) * Lss
B_init = np.ones((p.T + p.S, )) * Bss
L_init[:p.T] = aggr.get_L(n_mat[:p.T], p, 'TPI')
B_init[1:p.T] = aggr.get_K(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 * Kss / Bss
else:
K_init = firm.get_K(L_init, p.firm_r, p, 'TPI')
K = K_init
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:] = 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:] = 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:] = 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, p.debt_ratio_ss * Y)
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], BQss[j], p.T)) + [BQss[j]] * p.S)
BQ = np.array(BQ)
else:
BQ = (list(np.linspace(BQ0, BQss, p.T)) + [BQss] * p.S)
BQ = np.array(BQ)
if p.budget_balance:
if np.abs(T_Hss) < 1e-13:
T_Hss2 = 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:
T_Hss2 = T_Hss
T_H = np.ones(p.T + p.S) * T_Hss2
total_revenue = T_H
G = np.zeros(p.T + p.S)
elif not p.baseline_spending:
T_H = p.alpha_T * Y
elif p.baseline_spending:
T_H = T_Hbaseline
T_H_new = p.T_H # Need to set T_H_new for later reference
G = Gbaseline
G_0 = Gbaseline[0]
# Initialize some starting value
if p.budget_balance:
D = 0.0 * Y
else:
D = p.debt_ratio_ss * Y
TPIiter = 0
TPIdist = 10
euler_errors = np.zeros((p.T, 2 * p.S, p.J))
TPIdist_vec = np.zeros(p.maxiter)
print('analytical mtrs in tpi = ', p.analytical_mtrs)
print('tax function type in tpi = ', p.tax_func_type)
# 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, T_H, 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, :, :]
L[:p.T] = aggr.get_L(n_mat[:p.T], p, 'TPI')
B[1:p.T] = aggr.get_K(bmat_splus1[:p.T], p, 'TPI', False)[:p.T - 1]
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])
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')
tax_mat = tax.total_taxes(r_hh[:p.T], w[:p.T], bmat_s,
n_mat[:p.T, :, :], bqmat[:p.T, :, :], factor,
T_H[: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)
if not p.small_open:
if p.budget_balance:
K[:p.T] = B[:p.T]
else:
if not p.baseline_spending:
Y = T_H / p.alpha_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 = np.array(
list(total_rev) + [total_revenue_ss] * p.S)
# set intial debt value
if p.baseline:
D_0 = p.initial_debt_ratio * Y[0]
else:
D_0 = D0
if not p.baseline_spending:
G_0 = p.alpha_G[0] * Y[0]
dg_fixed_values = (Y, total_revenue, T_H, D_0, G_0)
Dnew, G = fiscal.D_G_path(r_gov, dg_fixed_values, Gbaseline, p)
K[:p.T] = B[:p.T] - Dnew[:p.T]
if np.any(K < 0):
print('K has negative elements. Setting them ' +
'positive to prevent NAN.')
K[:p.T] = np.fmax(K[:p.T], 0.05 * B[:p.T])
else:
K[:p.T] = firm.get_K(L[:p.T], p.firm_r[:p.T], p, 'TPI')
Ynew = firm.get_Y(K[:p.T], L[:p.T], p, 'TPI')
if not p.small_open:
rnew = firm.get_r(Ynew[:p.T], K[:p.T], p, 'TPI')
else:
rnew = r.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, r_gov_new, 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 = np.array(list(total_rev) + [total_revenue_ss] * p.S)
if p.budget_balance:
T_H_new = total_revenue
elif not p.baseline_spending:
T_H_new = p.alpha_T[:p.T] * Ynew[:p.T]
# If baseline_spending==True, no need to update T_H, it's fixed
if p.small_open and not p.budget_balance:
# Loop through years to calculate debt and gov't spending.
# This is done earlier when small_open=False.
if p.baseline:
D_0 = p.initial_debt_ratio * Y[0]
else:
D_0 = D0
if not p.baseline_spending:
G_0 = p.alpha_G[0] * Ynew[0]
dg_fixed_values = (Ynew, total_revenue, T_H, D_0, G_0)
Dnew, G = fiscal.D_G_path(r_gov_new, dg_fixed_values, Gbaseline, p)
if p.budget_balance:
Dnew = D
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 = Dnew
Y[:p.T] = utils.convex_combo(Ynew[:p.T], Y[:p.T], p.nu)
if not p.baseline_spending:
T_H[:p.T] = utils.convex_combo(T_H_new[:p.T], T_H[: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('T_H diff: ', (T_H_new[:p.T] - T_H[:p.T]).max(),
(T_H_new[:p.T] - T_H[: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 T_H.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(wnew[:p.T], w[:p.T])) +
list(utils.pct_diff_func(T_H_new[:p.T], T_H[: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(wnew[:p.T], w[:p.T])) +
list(np.abs(T_H[: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(wnew[:p.T], w[:p.T])) +
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')
if not p.small_open:
I = aggr.get_I(bmat_splus1[:p.T], K[1:p.T + 1], K[:p.T], p, 'TPI')
rc_error = Y[:p.T] - C[:p.T] - I[:p.T] - G[:p.T]
else:
I = ((1 + np.squeeze(np.hstack(
(p.g_n[1:p.T], p.g_n_ss)))) * np.exp(p.g_y) * K[1:p.T + 1] -
(1.0 - p.delta) * K[:p.T])
BI = aggr.get_I(bmat_splus1[:p.T], B[1:p.T + 1], B[:p.T], p, 'TPI')
new_borrowing = (D[1:p.T] * (1 + p.g_n[1:p.T]) * np.exp(p.g_y) -
D[:p.T - 1])
rc_error = (Y[:p.T - 1] + new_borrowing -
(C[:p.T - 1] + BI[:p.T - 1] + G[:p.T - 1]) +
(p.hh_r[:p.T - 1] * B[:p.T - 1] -
(p.delta + p.firm_r[:p.T - 1]) * K[:p.T - 1] -
p.hh_r[:p.T - 1] * D[:p.T - 1]))
# 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,
'L': L,
'C': C,
'I': I,
'I_total': I_total,
'BQ': BQ,
'total_revenue': total_revenue,
'business_revenue': business_revenue,
'IITpayroll_revenue': T_Ipath,
'T_H': T_H,
'T_P': T_Ppath,
'T_BQ': T_BQpath,
'T_W': T_Wpath,
'T_C': T_Cpath,
'G': G,
'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,
'tax_path': tax_mat,
'eul_savings': eul_savings,
'eul_laborleisure': eul_laborleisure,
'resource_constraint_error': rc_error,
'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 run_TPI(p, client=None):
# 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
(T_Hbaseline, 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_K(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_K(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['T_Hss']) < 1e-13:
T_Hss2 = 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:
T_Hss2 = ss_vars['T_Hss']
T_H = np.ones(p.T + p.S) * T_Hss2
total_revenue = T_H
G = np.zeros(p.T + p.S)
elif not p.baseline_spending:
T_H = p.alpha_T * Y
G = np.ones(p.T + p.S) * ss_vars['Gss']
elif p.baseline_spending:
T_H = T_Hbaseline
T_H_new = p.T_H # Need to set T_H_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, T_H, 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')
tax_mat = tax.total_taxes(r_hh[:p.T], w[:p.T], bmat_s,
n_mat[:p.T, :, :], bqmat[:p.T, :, :], factor,
T_H[: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] = T_H[: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, T_H, 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_K(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:
T_H_new = total_revenue
elif not p.baseline_spending:
T_H_new = p.alpha_T[:p.T] * Ynew[:p.T]
# If baseline_spending==True, no need to update T_H, 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:
T_H[:p.T] = utils.convex_combo(T_H_new[:p.T], T_H[: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('T_H diff: ', (T_H_new[:p.T] - T_H[:p.T]).max(),
(T_H_new[:p.T] - T_H[: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 T_H.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(T_H_new[:p.T], T_H[: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(T_H[: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,
'T_H': T_H,
'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,
'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