test_data = [(b1, p1, expected1),
(b2, p2, expected2)]
@pytest.mark.parametrize('b,p,expected', test_data,
ids=['constant params', 'vary params'])
def test_MTR_wealth(b, p, expected):
# Test marginal tax rate on wealth
tau_w_prime = tax.MTR_wealth(b, p.h_wealth[:p.T], p.m_wealth[:p.T],
p.p_wealth[:p.T])
assert np.allclose(tau_w_prime, expected)
p1 = Specifications()
p1.S = 2
p1.J = 1
p1.e = np.array([0.5, 0.45])
p1.tax_func_type = 'DEP'
etr_params1 = np.reshape(np.array([
[0.001, 0.002, 0.003, 0.0015, 0.8, -0.14, 0.8, -0.15, 0.15, 0.16,
-0.15, 0.83], [0.001, 0.002, 0.003, 0.0015, 0.8, -0.14, 0.8, -0.15,
0.15, 0.16, -0.15, 0.83]]), (1, p1.S, 12))
p2 = Specifications()
p2.S = 2
p2.J = 1
p2.e = np.array([0.5, 0.45])
p2.tax_func_type = 'GS'
etr_params2 = np.reshape(np.array([
[0.396, 0.7, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1.4248841, 0.806333875, 6.987729463]]))]
@pytest.mark.parametrize('n,params,expected',
test_data,
ids=['1', '2', '3', '4', '5', '6', '7', '8'])
def test_marg_ut_labor(n, params, expected):
# Test marginal utility of labor calculation
test_value = household.marg_ut_labor(n, params.chi_n, params)
assert np.allclose(test_value, expected)
p1 = Specifications()
p1.zeta = np.array([[0.1, 0.3], [0.15, 0.4], [0.05, 0.0]])
p1.S = 3
p1.J = 2
p1.T = 3
p1.lambdas = np.array([0.6, 0.4])
p1.omega_SS = np.array([0.25, 0.25, 0.5])
p1.omega = np.tile(p1.omega_SS.reshape((1, p1.S)), (p1.T, 1))
BQ1 = 2.5
p1.use_zeta = True
expected1 = np.array([[1.66666667, 7.5], [2.5, 10.0], [0.416666667, 0.0]])
p2 = Specifications()
p2.zeta = np.array([[0.1, 0.3], [0.15, 0.4], [0.05, 0.0]])
p2.S = 3
p2.J = 2
p2.T = 3
p2.lambdas = np.array([0.6, 0.4])
p2.omega_SS = np.array([0.25, 0.25, 0.5])