def test_cov_equiv_cluster(data):
mod = PooledOLS(data.y, data.x)
res = mod.fit(cov_type='clustered', cluster_entity=True, debiased=False)
y = PanelData(data.y)
clusters = pd.DataFrame(y.entity_ids, index=y.index)
res2 = mod.fit(cov_type='clustered', clusters=clusters, debiased=False)
assert_results_equal(res, res2)
res = mod.fit(cov_type='clustered', cluster_time=True, debiased=False)
clusters = pd.DataFrame(y.time_ids, index=y.index)
res2 = mod.fit(cov_type='clustered', clusters=clusters, debiased=False)
assert_results_equal(res, res2)
res = mod.fit(cov_type='clustered', clusters=data.vc1, debiased=False)
y = mod.dependent.dataframe.copy()
x = mod.exog.dataframe.copy()
y.index = np.arange(len(y))
x.index = y.index
clusters = mod.reformat_clusters(data.vc1)
ols_mod = IV2SLS(y, x, None, None)
res2 = ols_mod.fit(cov_type='clustered', clusters=clusters.dataframe,
debiased=False)
assert_results_equal(res, res2)
def test_multiple(data, precision):
dependent = data.set_index(["nr", "year"]).lwage
exog = add_constant(
data.set_index(["nr", "year"])[["expersq", "married", "union"]])
res = PanelOLS(dependent, exog, entity_effects=True,
time_effects=True).fit()
res2 = PanelOLS(dependent, exog,
entity_effects=True).fit(cov_type="clustered",
cluster_entity=True)
exog = add_constant(data.set_index(["nr", "year"])[["married", "union"]])
res3 = PooledOLS(dependent, exog).fit()
exog = data.set_index(["nr", "year"])[["exper"]]
res4 = RandomEffects(dependent, exog).fit()
comp = compare([res, res2, res3, res4], precision=precision)
assert len(comp.rsquared) == 4
d = dir(comp)
for value in d:
if value.startswith("_"):
continue
getattr(comp, value)
with pytest.raises(ValueError):
compare([res, res2, res3, res4], precision="unknown")
def test_multiple(data, precision):
dependent = data.set_index(['nr', 'year']).lwage
exog = sm.add_constant(
data.set_index(['nr', 'year'])[['expersq', 'married', 'union']])
res = PanelOLS(dependent, exog, entity_effects=True,
time_effects=True).fit()
res2 = PanelOLS(dependent, exog,
entity_effects=True).fit(cov_type='clustered',
cluster_entity=True)
exog = sm.add_constant(
data.set_index(['nr', 'year'])[['married', 'union']])
res3 = PooledOLS(dependent, exog).fit()
exog = data.set_index(['nr', 'year'])[['exper']]
res4 = RandomEffects(dependent, exog).fit()
comp = compare([res, res2, res3, res4], precision=precision)
assert len(comp.rsquared) == 4
d = dir(comp)
for value in d:
if value.startswith('_'):
continue
getattr(comp, value)
with pytest.raises(ValueError):
compare([res, res2, res3, res4], precision='unknown')
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_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_alt_rsquared_weighted(data):
mod = PooledOLS(data.y, data.x, weights=data.w)
res = mod.fit(debiased=False)
assert_allclose(res.rsquared, res.rsquared_overall)
def test_extra_df(data):
mod = PooledOLS(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))
def test_uneuqal_samples():
data = generate_data(False, "pandas")
with pytest.raises(ValueError, match="dependent and exog must have"):
PooledOLS(data.y.iloc[::2], data.x)
