def test_mv_reg_smoke(mvreg_data):
dependent, exog = mvreg_data
mod = SUR.multivariate_ls(dependent, exog)
mod.fit()
mod.fit(cov_type='unadjusted')
res = mod.fit(cov_type='unadjusted', method='ols')
assert res.method == 'OLS'
res = mod.fit(full_cov=False)
get_res(res)
def test_mv_reg_smoke(mvreg_data):
dependent, exog = mvreg_data
mod = SUR.multivariate_ls(dependent, exog)
mod.fit()
mod.fit(cov_type="unadjusted")
res = mod.fit(cov_type="unadjusted", method="ols")
assert res.method == "OLS"
res = mod.fit(full_cov=False)
get_res(res)
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)
def test_gls_without_mv_ols_equiv(mvreg_data):
dependent, exog = mvreg_data
mv_mod = SUR.multivariate_ls(dependent, exog)
mv_res = mv_mod.fit()
keys = mv_res.equation_labels
ad = AttrDict()
for i in range(dependent.shape[1]):
key = 'dependent.{0}'.format(i)
df = DataFrame(dependent[:, [i]], columns=[key])
ad[key] = {'dependent': df,
'exog': exog.copy()}
gls_mod = SUR(ad)
gls_res = gls_mod.fit(method='ols')
check_results(mv_res, gls_res)
for i in range(dependent.shape[1]):
mv_res_eq = mv_res.equations[keys[i]]
gls_res_eq = gls_res.equations[keys[i]]
check_results(mv_res_eq, gls_res_eq)
mv_res = mv_mod.fit(cov_type='robust')
gls_res = gls_mod.fit(cov_type='robust', method='ols')
check_results(mv_res, gls_res)
for i in range(dependent.shape[1]):
mv_res_eq = mv_res.equations[keys[i]]
gls_res_eq = gls_res.equations[keys[i]]
check_results(mv_res_eq, gls_res_eq)
mv_res = mv_mod.fit(cov_type='robust', debiased=True)
gls_res = gls_mod.fit(cov_type='robust', method='ols', debiased=True)
check_results(mv_res, gls_res)
for i in range(dependent.shape[1]):
mv_res_eq = mv_res.equations[keys[i]]
gls_res_eq = gls_res.equations[keys[i]]
check_results(mv_res_eq, gls_res_eq)
def test_gls_eye_mv_ols_equiv(mvreg_data):
dependent, exog = mvreg_data
mv_mod = SUR.multivariate_ls(dependent, exog)
mv_res = mv_mod.fit()
keys = mv_res.equation_labels
ad = AttrDict()
for i in range(dependent.shape[1]):
key = "dependent.{0}".format(i)
df = DataFrame(dependent[:, [i]], columns=[key])
ad[key] = {"dependent": df, "exog": exog.copy()}
gls_mod = SUR(ad, sigma=np.eye(len(ad)))
gls_res = gls_mod.fit(method="gls")
check_results(mv_res, gls_res)
for i in range(dependent.shape[1]):
mv_res_eq = mv_res.equations[keys[i]]
gls_res_eq = gls_res.equations[keys[i]]
check_results(mv_res_eq, gls_res_eq)
mv_res = mv_mod.fit(cov_type="robust")
gls_res = gls_mod.fit(cov_type="robust", method="gls")
check_results(mv_res, gls_res)
for i in range(dependent.shape[1]):
mv_res_eq = mv_res.equations[keys[i]]
gls_res_eq = gls_res.equations[keys[i]]
check_results(mv_res_eq, gls_res_eq)
mv_res = mv_mod.fit(cov_type="robust", debiased=True)
gls_res = gls_mod.fit(cov_type="robust", method="gls", debiased=True)
check_results(mv_res, gls_res)
for i in range(dependent.shape[1]):
mv_res_eq = mv_res.equations[keys[i]]
gls_res_eq = gls_res.equations[keys[i]]
check_results(mv_res_eq, gls_res_eq)
