def test_fama_macbeth(data):
res = FamaMacBeth(data.y, data.x).fit(debiased=True)
y = PanelData(data.y)
x = PanelData(data.x)
missing = y.isnull | x.isnull
y.drop(missing)
x.drop(missing)
y = y.dataframe
x = x.dataframe
times = y.index.levels[1]
params = []
for t in times:
_y = y.xs(t, level=1)
_x = x.xs(t, level=1)
if _x.shape[0] < _x.shape[1]:
continue
_x = _x.loc[_y.index]
params.append(lstsq(_x.values, _y.values, rcond=None)[0])
params = np.array(params).squeeze()
all_params = params
params = params.mean(0)
assert_allclose(params.squeeze(), res.params)
assert_allclose(all_params, res.all_params.dropna(how="all"))
e_params = all_params - params[None, :]
ntime = e_params.shape[0]
cov = e_params.T @ e_params / ntime / (ntime - 1)
assert_allclose(cov, np.asarray(res.cov))
access_attributes(res)
def test_fama_macbeth_kernel_smoke(data):
FamaMacBeth(data.y, data.x).fit(cov_type="kernel")
FamaMacBeth(data.y, data.x).fit(cov_type="kernel", kernel="bartlett")
FamaMacBeth(data.y, data.x).fit(cov_type="kernel", kernel="newey-west")
FamaMacBeth(data.y, data.x).fit(cov_type="kernel", kernel="parzen")
FamaMacBeth(data.y, data.x).fit(cov_type="kernel", kernel="qs")
FamaMacBeth(data.y, data.x).fit(cov_type="kernel", bandwidth=3)
res = FamaMacBeth(data.y, data.x).fit(cov_type="kernel", kernel="andrews")
access_attributes(res)
def test_fama_macbeth_kernel_smoke(data):
FamaMacBeth(data.y, data.x).fit(cov_type='kernel')
FamaMacBeth(data.y, data.x).fit(cov_type='kernel', kernel='bartlett')
FamaMacBeth(data.y, data.x).fit(cov_type='kernel', kernel='newey-west')
FamaMacBeth(data.y, data.x).fit(cov_type='kernel', kernel='parzen')
FamaMacBeth(data.y, data.x).fit(cov_type='kernel', kernel='qs')
FamaMacBeth(data.y, data.x).fit(cov_type='kernel', bandwidth=3)
res = FamaMacBeth(data.y, data.x).fit(cov_type='kernel', kernel='andrews')
access_attributes(res)
def test_results_access(data):
mod = PanelOLS(data.y, data.x, entity_effects=True)
res = mod.fit()
access_attributes(res)
mod = PanelOLS(data.y, data.x, other_effects=data.c)
res = mod.fit()
access_attributes(res)
mod = PanelOLS(data.y, data.x, time_effects=True, entity_effects=True)
res = mod.fit()
access_attributes(res)
mod = PanelOLS(data.y, data.x)
res = mod.fit()
access_attributes(res)
const = PanelData(data.y).copy()
const.dataframe.iloc[:, :] = 1
const.dataframe.columns = ["const"]
mod = PanelOLS(data.y, const)
res = mod.fit()
access_attributes(res)
def test_results_access(data):
mod = PooledOLS(data.y, data.x)
res = mod.fit(debiased=False)
access_attributes(res)
mod = PooledOLS(data.y, data.x)
res = mod.fit(debiased=True)
access_attributes(res)
if not isinstance(data.x, pd.DataFrame):
return
x = data.y.copy()
x.iloc[:, :] = 1
mod = PooledOLS(data.y, x)
res = mod.fit(debiased=False)
access_attributes(res)
def test_results_access(data):
mod = FirstDifferenceOLS(data.y, data.x)
res = mod.fit(debiased=False)
access_attributes(res)
def test_results_access(data):
mod = RandomEffects(data.y, data.x)
res = mod.fit(debiased=False)
access_attributes(res)
def test_results_access(data):
mod = BetweenOLS(data.y, data.x)
res = mod.fit(debiased=False)
access_attributes(res)
