def test_ETR_income(b, n, etr_params, params, expected):
# Test income tax function
r = 0.04
w = 1.2
factor = 100000
test_ETR_income = tax.ETR_income(r, w, b, n, factor, params.e,
etr_params, params)
assert np.allclose(test_ETR_income, expected)
p1 = Specifications()
p1.e = np.array([0.5, 0.45, 0.3])
p1.S = 3
p1.J = 1
p1.tax_func_type = 'DEP'
p1.analytical_mtrs = True
etr_params1 = np.reshape(np.array([
[0.001, 0.002, 0.003, 0.0015, 0.8,
0.8, 0.83, -0.14, -0.15, 0.15, 0.16, -0.15],
[0.002, 0.001, 0.002, 0.04, 0.8,
0.8, 0.83, -0.14, -0.15, 0.15, 0.16, -0.15],
[0.011, 0.001, 0.003, 0.06, 0.8,
0.8, 0.83, -0.14, -0.15, 0.15, 0.16, -0.15]]), (1, p1.S, 12))
mtrx_params1 = np.reshape(np.array([
[0.001, 0.002, 0.003, 0.0015, 0.68,
0.8, 0.96, -0.17, -0.42, 0.18, 0.43, -0.42],
[0.001, 0.002, 0.003, 0.0015, 0.65,
0.8, 0.90, -0.17, -0.42, 0.18, 0.33, -0.12],
[0.001, 0.002, 0.003, 0.0015, 0.56,
0.8, 0.65, -0.17, -0.42, 0.18, 0.38, -0.22]]), (1, p1.S, 12))
p2 = Specifications()
-------------------------------------------------------------------------------
'''
# Define variables for test of SS version
p1 = Specifications()
p1.e = np.array([1.0, 0.9, 1.4]).reshape(3, 1)
p1.sigma = 2.0
p1.J = 1
p1.beta = np.ones(p1.J) * 0.96
p1.g_y = 0.03
p1.chi_b = np.array([1.5])
p1.tau_bq = np.array([0.0])
p1.rho = np.array([0.1, 0.2, 1.0])
p1.lambdas = np.array([1.0])
p1.S = 3
p1.T = 3
p1.analytical_mtrs = False
etr_params = np.array([
np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.33, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.25, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.20, 0]]),
np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0]]),
np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.15, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.45, 0]])
])
mtry_params = np.array([
np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.45, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.28, 0]]),
