def test_panel_no_effects(data):
res = PanelOLS(data.y, data.x).fit()
res2 = PooledOLS(data.y, data.x).fit()
assert_results_equal(res, res2)
def test_panel_other_lsdv(data):
mod = PanelOLS(data.y, data.x, other_effects=data.c)
assert 'Num Other Effects: 2' in str(mod)
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 @ 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(cov_type='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(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_lsdv_options(data):
mod = PanelOLS(data.y, data.x, weights=data.w)
res1 = mod.fit()
res2 = mod.fit(use_lsdv=True)
assert_results_equal(res1, res2)
mod = PanelOLS(data.y, data.x, weights=data.w, entity_effects=True)
res1 = mod.fit()
res2 = mod.fit(use_lsdv=True)
assert_results_equal(res1, res2)
mod = PanelOLS(data.y, data.x, time_effects=True)
res1 = mod.fit()
res2 = mod.fit(use_lsdv=True)
assert_results_equal(res1, res2)
mod = PanelOLS(data.y, data.x, time_effects=True, entity_effects=True)
res1 = mod.fit()
res2 = mod.fit(use_lsdv=True)
assert_results_equal(res1, res2)
c1 = PanelData(data.c).dataframe.iloc[:, [0]]
mod = PanelOLS(data.y, data.x, entity_effects=True, other_effects=c1)
res1 = mod.fit()
res2 = mod.fit(use_lsdv=True)
assert_results_equal(res1, res2)
mod = PanelOLS(data.y, data.x, time_effects=True, other_effects=c1)
res1 = mod.fit()
res2 = mod.fit(use_lsdv=True)
assert_results_equal(res1, res2)
mod = PanelOLS(data.y, data.x, weights=data.w, entity_effects=True, other_effects=c1)
res1 = mod.fit()
res2 = mod.fit(use_lsdv=True)
assert_results_equal(res1, res2)
mod = PanelOLS(data.y, data.x, weights=data.w, time_effects=True, other_effects=c1)
res1 = mod.fit()
res2 = mod.fit(use_lsdv=True)
assert_results_equal(res1, res2)
mod = PanelOLS(data.y, data.x, weights=data.w, other_effects=data.c)
res1 = mod.fit()
res2 = mod.fit(use_lsdv=True)
assert_results_equal(res1, res2)
def test_panel_time_lsdv(large_data):
mod = PanelOLS(large_data.y, large_data.x, time_effects=True)
res = mod.fit(auto_df=False, count_effects=False, debiased=False)
y = mod.dependent.dataframe
x = mod.exog.dataframe
d = mod.dependent.dummies('time', drop_first=mod.has_constant)
d_cols = list(d.columns)
d = d.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) + d_cols)
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 = 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_both_lsdv_weighted(data):
mod = PanelOLS(data.y, data.x, entity_effects=True, time_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
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)
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(d1.columns) + list(d2.columns))
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_no_effects_weighted(data):
res = PanelOLS(data.y, data.x, weights=data.w).fit()
res2 = PooledOLS(data.y, data.x, weights=data.w).fit()
assert_results_equal(res, res2)
def test_firstdifference_ols_weighted(data):
mod = FirstDifferenceOLS(data.y, data.x, weights=data.w)
res = mod.fit(debiased=False)
y = mod.dependent.values3d
x = mod.exog.values3d
dy = np.array(y[0, 1:] - y[0, :-1])
dy = pd.DataFrame(
dy,
index=mod.dependent.panel.major_axis[1:],
columns=mod.dependent.panel.minor_axis,
)
dy = dy.T.stack()
dy = dy.reindex(mod.dependent.index)
dx = x[:, 1:] - x[:, :-1]
_dx = {}
for i, dxi in enumerate(dx):
temp = pd.DataFrame(
dxi,
index=mod.dependent.panel.major_axis[1:],
columns=mod.dependent.panel.minor_axis,
)
temp = temp.T.stack()
temp = temp.reindex(mod.dependent.index)
_dx[mod.exog.vars[i]] = temp
dx = pd.DataFrame(index=_dx[mod.exog.vars[i]].index)
for key in _dx:
dx[key] = _dx[key]
dx = dx[mod.exog.vars]
w = mod.weights.values3d
w = 1.0 / w
sw = w[0, 1:] + w[0, :-1]
sw = pd.DataFrame(
sw,
index=mod.dependent.panel.major_axis[1:],
columns=mod.dependent.panel.minor_axis,
)
sw = sw.T.stack()
sw = sw.reindex(mod.dependent.index)
sw = 1.0 / sw
sw = sw / sw.mean()
drop = dy.isnull() | np.any(dx.isnull(), 1) | sw.isnull()
dy = dy.loc[~drop]
dx = dx.loc[~drop]
sw = sw.loc[~drop]
ols_mod = IV2SLS(dy, dx, None, None, weights=sw)
ols_res = ols_mod.fit(cov_type="unadjusted")
assert_results_equal(res, ols_res)
res = mod.fit(cov_type="robust", debiased=False)
ols_res = ols_mod.fit(cov_type="robust")
assert_results_equal(res, ols_res)
clusters = data.vc1
ols_clusters = mod.reformat_clusters(data.vc1)
fd = mod.dependent.first_difference()
ols_clusters = ols_clusters.dataframe.loc[fd.index]
res = mod.fit(cov_type="clustered", clusters=clusters, debiased=False)
ols_res = ols_mod.fit(cov_type="clustered", clusters=ols_clusters)
assert_results_equal(res, ols_res)
def test_firstdifference_ols(data):
mod = FirstDifferenceOLS(data.y, data.x)
res = mod.fit(debiased=False)
y = mod.dependent.values3d
x = mod.exog.values3d
dy = np.array(y[0, 1:] - y[0, :-1])
dy = pd.DataFrame(
dy,
index=mod.dependent.panel.major_axis[1:],
columns=mod.dependent.panel.minor_axis,
)
dy = dy.T.stack()
dy = dy.reindex(mod.dependent.index)
dx = x[:, 1:] - x[:, :-1]
_dx = {}
for i, dxi in enumerate(dx):
temp = pd.DataFrame(
dxi,
index=mod.dependent.panel.major_axis[1:],
columns=mod.dependent.panel.minor_axis,
)
temp = temp.T.stack()
temp = temp.reindex(mod.dependent.index)
_dx[mod.exog.vars[i]] = temp
dx = pd.DataFrame(index=_dx[mod.exog.vars[i]].index)
for key in _dx:
dx[key] = _dx[key]
dx = dx[mod.exog.vars]
drop = dy.isnull() | np.any(dx.isnull(), 1)
dy = dy.loc[~drop]
dx = dx.loc[~drop]
ols_mod = IV2SLS(dy, dx, None, None)
ols_res = ols_mod.fit(cov_type="unadjusted")
assert_results_equal(res, ols_res)
res = mod.fit(cov_type="robust", debiased=False)
ols_res = ols_mod.fit(cov_type="robust")
assert_results_equal(res, ols_res)
clusters = data.vc1
ols_clusters = mod.reformat_clusters(data.vc1)
fd = mod.dependent.first_difference()
ols_clusters = ols_clusters.dataframe.loc[fd.index]
res = mod.fit(cov_type="clustered", clusters=clusters, debiased=False)
ols_res = ols_mod.fit(cov_type="clustered", clusters=ols_clusters)
assert_results_equal(res, ols_res)
res = mod.fit(cov_type="clustered", cluster_entity=True, debiased=False)
entity_clusters = mod.dependent.first_difference().entity_ids
ols_res = ols_mod.fit(cov_type="clustered", clusters=entity_clusters)
assert_results_equal(res, ols_res)
ols_clusters["entity.clusters"] = entity_clusters
ols_clusters = ols_clusters.astype(np.int32)
res = mod.fit(cov_type="clustered",
cluster_entity=True,
clusters=data.vc1,
debiased=False)
ols_res = ols_mod.fit(cov_type="clustered", clusters=ols_clusters)
assert_results_equal(res, ols_res)
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 @ np.linalg.lstsq(z, d.values)[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)
def test_absorbed_option(data):
mod = PanelOLS(data.y, data.x, entity_effects=True, drop_absorbed=True)
res = mod.fit(auto_df=False, count_effects=False, debiased=False)
mod = PanelOLS(data.y, data.x, entity_effects=True, drop_absorbed=False)
res_false = mod.fit(auto_df=False, count_effects=False, debiased=False)
assert_results_equal(res, res_false)
