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)
