def test_ols_against_gls(data):
mod = SUR(data)
res = mod.fit(method='gls')
sigma = res.sigma
sigma_m12 = inv_matrix_sqrt(sigma)
key = list(data.keys())[0]
if isinstance(data[key], Mapping):
y = [data[key]['dependent'] for key in data]
x = [data[key]['exog'] for key in data]
try:
w = [data[key]['weights'] for key in data]
except KeyError:
w = [np.ones_like(data[key]['dependent']) for key in data]
else:
y = [data[key][0] for key in data]
x = [data[key][1] for key in data]
try:
w = [data[key][2] for key in data]
except IndexError:
w = [np.ones_like(data[key][0]) for key in data]
wy = [_y * np.sqrt(_w / _w.mean()) for _y, _w in zip(y, w)]
wx = [_x * np.sqrt(_w / _w.mean()) for _x, _w in zip(x, w)]
wy = blocked_column_product(wy, sigma_m12)
wx = blocked_diag_product(wx, sigma_m12)
ols_res = OLS(wy, wx).fit(debiased=False)
assert_allclose(res.params, ols_res.params)
def test_ols_against_gls(data):
mod = SUR(data)
res = mod.fit(method="gls")
if isinstance(data[list(data.keys())[0]], dict):
predictions = mod.predict(res.params, equations=data)
predictions2 = mod.predict(np.asarray(res.params)[:, None],
equations=data)
assert_allclose(predictions, predictions2)
sigma = res.sigma
sigma_m12 = inv_matrix_sqrt(np.asarray(sigma))
key = list(data.keys())[0]
if isinstance(data[key], Mapping):
y = [data[key]["dependent"] for key in data]
x = [data[key]["exog"] for key in data]
try:
w = [data[key]["weights"] for key in data]
except KeyError:
w = [np.ones_like(data[key]["dependent"]) for key in data]
else:
y = [data[key][0] for key in data]
x = [data[key][1] for key in data]
try:
w = [data[key][2] for key in data]
except IndexError:
w = [np.ones_like(data[key][0]) for key in data]
wy = [_y * np.sqrt(_w / _w.mean()) for _y, _w in zip(y, w)]
wx = [_x * np.sqrt(_w / _w.mean()) for _x, _w in zip(x, w)]
wy = blocked_column_product(wy, sigma_m12)
wx = blocked_diag_product(wx, sigma_m12)
ols_res = OLS(wy, wx).fit(debiased=False)
assert_allclose(res.params, ols_res.params)
def test_mv_ols_equivalence_hetero_debiased(mvreg_data):
dependent, exog = mvreg_data
mod = SUR.multivariate_ls(dependent, exog)
res = mod.fit(cov_type='robust', debiased=True)
keys = res.equation_labels
for i in range(dependent.shape[1]):
ols_mod = OLS(dependent[:, i], exog)
ols_res = ols_mod.fit(cov_type='robust', debiased=True)
mv_res = res.equations[keys[i]]
check_results(mv_res, ols_res)
def test_mv_ols_equivalence(mvreg_data):
dependent, exog = mvreg_data
mod = SUR.multivariate_ls(dependent, exog)
res = mod.fit(cov_type='unadjusted')
keys = res.equation_labels
assert res.method == 'OLS'
for i in range(dependent.shape[1]):
ols_mod = OLS(dependent[:, i], exog)
ols_res = ols_mod.fit(cov_type='unadjusted', debiased=False)
mv_res = res.equations[keys[i]]
assert mv_res.method == 'OLS'
check_results(mv_res, ols_res)