def test_panel_other_lsdv(data):
mod = PanelOLS(data.y, data.x, other_effects=data.c)
res = mod.fit(auto_df=False, count_effects=False, debiased=False)
y = mod.dependent.dataframe.copy()
x = mod.exog.dataframe.copy()
c = mod._other_effect_cats.dataframe.copy()
d = []
d_columns = []
for i, col in enumerate(c):
s = c[col].copy()
dummies = pd.get_dummies(s.astype(np.int64), drop_first=(mod.has_constant or i > 0))
dummies.columns = [s.name + '_val_' + str(c) for c in dummies.columns]
d_columns.extend(list(dummies.columns))
d.append(dummies.values)
d = np.column_stack(d)
if mod.has_constant:
z = np.ones_like(y)
d = d - z @ np.linalg.lstsq(z, d)[0]
xd = np.c_[x.values, d]
xd = pd.DataFrame(xd, index=x.index, columns=list(x.columns) + list(d_columns))
ols_mod = IV2SLS(y, xd, None, None)
res2 = ols_mod.fit('unadjusted')
assert_results_equal(res, res2, test_fit=False)
res3 = mod.fit(cov_type='unadjusted', auto_df=False, count_effects=False, debiased=False)
assert_results_equal(res, res3)
res = mod.fit(cov_type='robust', auto_df=False, count_effects=False, debiased=False)
res2 = ols_mod.fit('robust')
assert_results_equal(res, res2, test_fit=False)
clusters = data.vc1
ols_clusters = mod.reformat_clusters(clusters)
res = mod.fit(cov_type='clustered', clusters=clusters, auto_df=False, count_effects=False, debiased=False)
res2 = ols_mod.fit('clustered', clusters=ols_clusters.dataframe)
assert_results_equal(res, res2, test_fit=False)
clusters = data.vc2
ols_clusters = mod.reformat_clusters(clusters)
res = mod.fit(cov_type='clustered', clusters=clusters, auto_df=False, count_effects=False, debiased=False)
res2 = ols_mod.fit('clustered', clusters=ols_clusters.dataframe)
assert_results_equal(res, res2, test_fit=False)
res = mod.fit(cov_type='clustered', cluster_time=True, auto_df=False, count_effects=False, debiased=False)
clusters = pd.DataFrame(mod.dependent.time_ids,
index=mod.dependent.index,
columns=['var.clust'])
res2 = ols_mod.fit('clustered', clusters=clusters)
assert_results_equal(res, res2, test_fit=False)
res = mod.fit(cov_type='clustered', cluster_entity=True, auto_df=False, count_effects=False, debiased=False)
clusters = pd.DataFrame(mod.dependent.entity_ids,
index=mod.dependent.index,
columns=['var.clust'])
res2 = ols_mod.fit('clustered', clusters=clusters)
assert_results_equal(res, res2, test_fit=False)
def test_panel_entity_lsdv_weighted(data):
mod = PanelOLS(data.y, data.x, entity_effects=True, weights=data.w)
res = mod.fit(auto_df=False, count_effects=False, debiased=False)
y = mod.dependent.dataframe
x = mod.exog.dataframe
w = mod.weights.dataframe
d = mod.dependent.dummies('entity', drop_first=mod.has_constant)
d_cols = d.columns
d = d.values
if mod.has_constant:
z = np.ones_like(y)
root_w = np.sqrt(w.values)
wd = root_w * d
wz = root_w * z
d = d - z @ lstsq(wz, wd)[0]
xd = np.c_[x.values, d]
xd = pd.DataFrame(xd, index=x.index, columns=list(x.columns) + list(d_cols))
ols_mod = IV2SLS(y, xd, None, None, weights=w)
res2 = ols_mod.fit(cov_type='unadjusted')
assert_results_equal(res, res2, test_fit=False)
assert_allclose(res.rsquared_inclusive, res2.rsquared)
res = mod.fit(cov_type='robust', auto_df=False, count_effects=False, debiased=False)
res2 = ols_mod.fit(cov_type='robust')
assert_results_equal(res, res2, test_fit=False)
clusters = data.vc1
ols_clusters = mod.reformat_clusters(clusters)
res = mod.fit(cov_type='clustered', clusters=clusters, auto_df=False, count_effects=False,
debiased=False)
res2 = ols_mod.fit(cov_type='clustered', clusters=ols_clusters.dataframe)
assert_results_equal(res, res2, test_fit=False)
clusters = data.vc2
ols_clusters = mod.reformat_clusters(clusters)
res = mod.fit(cov_type='clustered', clusters=clusters, auto_df=False, count_effects=False,
debiased=False)
res2 = ols_mod.fit(cov_type='clustered', clusters=ols_clusters.dataframe)
assert_results_equal(res, res2, test_fit=False)
res = mod.fit(cov_type='clustered', cluster_time=True, auto_df=False, count_effects=False,
debiased=False)
clusters = pd.DataFrame(mod.dependent.time_ids,
index=mod.dependent.index,
columns=['var.clust'])
res2 = ols_mod.fit(cov_type='clustered', clusters=clusters)
assert_results_equal(res, res2, test_fit=False)
res = mod.fit(cov_type='clustered', cluster_entity=True, auto_df=False, count_effects=False,
debiased=False)
clusters = pd.DataFrame(mod.dependent.entity_ids,
index=mod.dependent.index,
columns=['var.clust'])
res2 = ols_mod.fit(cov_type='clustered', clusters=clusters)
assert_results_equal(res, res2, test_fit=False)
def test_panel_both_lsdv(data):
mod = PanelOLS(data.y, data.x, entity_effects=True, time_effects=True)
res = mod.fit(auto_df=False, count_effects=False, debiased=False)
y = mod.dependent.dataframe
x = mod.exog.dataframe
d1 = mod.dependent.dummies('entity', drop_first=mod.has_constant)
d2 = mod.dependent.dummies('time', drop_first=True)
d = np.c_[d1.values, d2.values]
if mod.has_constant:
z = np.ones_like(y)
d = d - z @ lstsq(z, d)[0]
xd = np.c_[x.values, d]
xd = pd.DataFrame(xd,
index=x.index,
columns=list(x.columns) + list(d1.columns) + list(d2.columns))
ols_mod = IV2SLS(y, xd, None, None)
res2 = ols_mod.fit(cov_type='unadjusted')
assert_results_equal(res, res2, test_fit=False)
assert_allclose(res.rsquared_inclusive, res2.rsquared)
res = mod.fit(cov_type='robust', auto_df=False, count_effects=False, debiased=False)
res2 = ols_mod.fit(cov_type='robust')
assert_results_equal(res, res2, test_fit=False)
clusters = data.vc1
ols_clusters = mod.reformat_clusters(clusters)
res = mod.fit(cov_type='clustered', clusters=clusters, auto_df=False, count_effects=False,
debiased=False)
res2 = ols_mod.fit(cov_type='clustered', clusters=ols_clusters.dataframe)
assert_results_equal(res, res2, test_fit=False)
clusters = data.vc2
ols_clusters = mod.reformat_clusters(clusters)
res = mod.fit(cov_type='clustered', clusters=clusters, auto_df=False, count_effects=False,
debiased=False)
res2 = ols_mod.fit(cov_type='clustered', clusters=ols_clusters.dataframe)
assert_results_equal(res, res2, test_fit=False)
res = mod.fit(cov_type='clustered', cluster_time=True, auto_df=False, count_effects=False,
debiased=False)
clusters = pd.DataFrame(mod.dependent.time_ids,
index=mod.dependent.index,
columns=['var.clust'])
res2 = ols_mod.fit(cov_type='clustered', clusters=clusters)
assert_results_equal(res, res2, test_fit=False)
res = mod.fit(cov_type='clustered', cluster_entity=True, auto_df=False, count_effects=False,
debiased=False)
clusters = pd.DataFrame(mod.dependent.entity_ids,
index=mod.dependent.index,
columns=['var.clust'])
res2 = ols_mod.fit(cov_type='clustered', clusters=clusters)
assert_results_equal(res, res2, test_fit=False)
def test_panel_time_lsdv_weighted(large_data):
mod = PanelOLS(large_data.y,
large_data.x,
time_effects=True,
weights=large_data.w)
res = mod.fit(auto_df=False, count_effects=False, debiased=False)
y = mod.dependent.dataframe
x = mod.exog.dataframe
w = mod.weights.dataframe
d = mod.dependent.dummies("time", drop_first=mod.has_constant)
d_cols = d.columns
d = d.values
if mod.has_constant:
z = np.ones_like(y)
root_w = np.sqrt(w.values)
wd = root_w * d
wz = root_w * z
d = d - z @ lstsq(wz, wd, rcond=None)[0]
xd = np.c_[x.values, d]
xd = pd.DataFrame(xd,
index=x.index,
columns=list(x.columns) + list(d_cols))
ols_mod = IV2SLS(y, xd, None, None, weights=w)
res2 = ols_mod.fit(cov_type="unadjusted")
assert_results_equal(res, res2, test_fit=False)
res = mod.fit(cov_type="robust",
auto_df=False,
count_effects=False,
debiased=False)
res2 = ols_mod.fit(cov_type="robust")
assert_results_equal(res, res2, test_fit=False)
clusters = large_data.vc1
ols_clusters = mod.reformat_clusters(clusters)
res = mod.fit(
cov_type="clustered",
clusters=clusters,
auto_df=False,
count_effects=False,
debiased=False,
)
res2 = ols_mod.fit(cov_type="clustered", clusters=ols_clusters.dataframe)
assert_results_equal(res, res2, test_fit=False)
clusters = large_data.vc2
ols_clusters = mod.reformat_clusters(clusters)
res = mod.fit(
cov_type="clustered",
clusters=clusters,
auto_df=False,
count_effects=False,
debiased=False,
)
res2 = ols_mod.fit(cov_type="clustered", clusters=ols_clusters.dataframe)
assert_results_equal(res, res2, test_fit=False)
res = mod.fit(
cov_type="clustered",
cluster_time=True,
auto_df=False,
count_effects=False,
debiased=False,
)
clusters = pd.DataFrame(mod.dependent.time_ids,
index=mod.dependent.index,
columns=["var.clust"])
res2 = ols_mod.fit(cov_type="clustered", clusters=clusters)
assert_results_equal(res, res2, test_fit=False)
res = mod.fit(
cov_type="clustered",
cluster_entity=True,
auto_df=False,
count_effects=False,
debiased=False,
)
clusters = pd.DataFrame(mod.dependent.entity_ids,
index=mod.dependent.index,
columns=["var.clust"])
res2 = ols_mod.fit(cov_type="clustered", clusters=clusters)
assert_results_equal(res, res2, test_fit=False)
def test_panel_entity_lsdv(data):
mod = PanelOLS(data.y, data.x, entity_effects=True)
res = mod.fit(auto_df=False, count_effects=False, debiased=False)
y = mod.dependent.dataframe
x = mod.exog.dataframe
if mod.has_constant:
d = mod.dependent.dummies("entity", drop_first=True)
z = np.ones_like(y)
d_demean = d.values - z @ lstsq(z, d.values, rcond=None)[0]
else:
d = mod.dependent.dummies("entity", drop_first=False)
d_demean = d.values
xd = np.c_[x.values, d_demean]
xd = pd.DataFrame(xd,
index=x.index,
columns=list(x.columns) + list(d.columns))
ols_mod = IV2SLS(y, xd, None, None)
res2 = ols_mod.fit(cov_type="unadjusted", debiased=False)
assert_results_equal(res, res2, test_fit=False)
assert_allclose(res.rsquared_inclusive, res2.rsquared)
res = mod.fit(cov_type="robust",
auto_df=False,
count_effects=False,
debiased=False)
res2 = ols_mod.fit(cov_type="robust")
assert_results_equal(res, res2, test_fit=False)
clusters = data.vc1
ols_clusters = mod.reformat_clusters(data.vc1)
res = mod.fit(
cov_type="clustered",
clusters=clusters,
auto_df=False,
count_effects=False,
debiased=False,
)
res2 = ols_mod.fit(cov_type="clustered", clusters=ols_clusters.dataframe)
assert_results_equal(res, res2, test_fit=False)
clusters = data.vc2
ols_clusters = mod.reformat_clusters(data.vc2)
res = mod.fit(
cov_type="clustered",
clusters=clusters,
auto_df=False,
count_effects=False,
debiased=False,
)
res2 = ols_mod.fit(cov_type="clustered", clusters=ols_clusters.dataframe)
assert_results_equal(res, res2, test_fit=False)
res = mod.fit(
cov_type="clustered",
cluster_time=True,
auto_df=False,
count_effects=False,
debiased=False,
)
clusters = pd.DataFrame(mod.dependent.time_ids,
index=mod.dependent.index,
columns=["var.clust"])
res2 = ols_mod.fit(cov_type="clustered", clusters=clusters)
assert_results_equal(res, res2, test_fit=False)
res = mod.fit(
cov_type="clustered",
cluster_entity=True,
auto_df=False,
count_effects=False,
debiased=False,
)
clusters = pd.DataFrame(mod.dependent.entity_ids,
index=mod.dependent.index,
columns=["var.clust"])
res2 = ols_mod.fit(cov_type="clustered", clusters=clusters)
assert_results_equal(res, res2, test_fit=False)
