def test_results_access(data):
mod = FirstDifferenceOLS(data.y, data.x)
res = mod.fit(debiased=False)
d = dir(res)
for key in d:
if not key.startswith('_'):
val = getattr(res, key)
if callable(val):
val()
def test_firstdifference_error(data):
mod = FirstDifferenceOLS(data.y, data.x)
clusters = mod.dependent.dataframe.copy()
for entity in mod.dependent.entities:
clusters.loc[entity] = np.random.randint(9)
clusters.iloc[::3, :] = clusters.iloc[::3, :] + 1
with pytest.raises(ValueError):
mod.fit(cov_type='clustered', clusters=clusters)
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_first_difference_errors(data):
if isinstance(data.x, pd.Panel):
x = data.x.iloc[:, [0], :]
y = data.y.iloc[[0], :]
else:
x = data.x[:, [0], :]
y = data.y[[0], :]
with pytest.raises(ValueError):
FirstDifferenceOLS(y, x)
if not isinstance(data.x, pd.Panel):
return
x = data.x.copy()
x['Intercept'] = 1.0
with pytest.raises(ValueError):
FirstDifferenceOLS(data.y, x)
def test_first_difference_errors(data):
if isinstance(data.x, pd.DataFrame):
time = data.y.index.levels[1][0]
y = data.y.xs(time, level=1, drop_level=False)
x = data.x.xs(time, level=1, drop_level=False)
else:
x = data.x[:, [0], :]
y = data.y[[0], :]
with pytest.raises(ValueError):
FirstDifferenceOLS(y, x)
if not isinstance(data.x, pd.DataFrame):
return
x = data.x.copy()
x['Intercept'] = 1.0
with pytest.raises(ValueError):
FirstDifferenceOLS(data.y, x)
def test_fitted_effects_residuals(data):
mod = FirstDifferenceOLS(data.y, data.x)
res = mod.fit()
expected = mod.exog.values2d @ res.params.values
expected = pd.DataFrame(expected, index=mod.exog.index, columns=['fitted_values'])
assert_allclose(res.fitted_values, expected)
assert_frame_similar(res.fitted_values, expected)
expected.iloc[:, 0] = mod.dependent.values2d - expected.values
expected.columns = ['idiosyncratic']
assert_allclose(res.idiosyncratic, expected)
assert_frame_similar(res.idiosyncratic, expected)
expected.iloc[:, 0] = np.nan
expected.columns = ['estimated_effects']
assert_allclose(res.estimated_effects, expected)
assert_frame_similar(res.estimated_effects, expected)
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_results_access(data):
mod = FirstDifferenceOLS(data.y, data.x)
res = mod.fit(debiased=False)
access_attributes(res)
def test_extra_df(data):
mod = FirstDifferenceOLS(data.y, data.x)
res = mod.fit()
res_extra = mod.fit(extra_df=10)
assert np.all(np.diag(res_extra.cov) > np.diag(res.cov))
