def test_demean_many_missing(mi_df):
entities = mi_df.index.levels[0]
times = mi_df.index.levels[1]
skips = (3, 5, 2)
for column, skip in zip(mi_df, skips):
for entity in entities[::skip]:
mi_df.loc[entity, column] = np.nan
mi_df.index = mi_df.index.swaplevel()
for time in times[::skip]:
mi_df.loc[time, column] = np.nan
mi_df.index = mi_df.index.swaplevel()
data = PanelData(mi_df)
fe = data.demean("entity")
orig_nan = np.isnan(data.values3d.ravel())
fe_nan = np.isnan(fe.values3d.ravel())
assert np.all(fe_nan[orig_nan])
expected = data.values3d.copy()
for i in range(3):
mu = np.ones(expected[i].shape[1]) * np.nan
for j in range(expected[i].shape[1]):
if np.any(np.isfinite(expected[i][:, j])):
mu[j] = np.nanmean(expected[i][:, j])
expected[i] -= mu
assert_allclose(fe.values3d, expected)
te = data.demean("time")
expected = data.values3d.copy()
for i in range(3):
mu = np.ones((expected[i].shape[0], 1)) * np.nan
for j in range(expected[i].shape[0]):
if np.any(np.isfinite(expected[i][j])):
mu[j, 0] = np.nanmean(expected[i][j])
expected[i] -= mu
assert_allclose(te.values3d, expected)
def test_demean_against_dummy_regression(data):
dh = PanelData(data.x)
dh.drop(dh.isnull)
df = dh.dataframe
no_index = df.reset_index()
cat = Categorical(no_index[df.index.levels[0].name])
d = get_dummies(cat, drop_first=False).astype(np.float64)
dummy_demeaned = df.values - d @ lstsq(d, df.values, rcond=None)[0]
entity_demean = dh.demean("entity")
assert_allclose(1 + np.abs(entity_demean.values2d),
1 + np.abs(dummy_demeaned))
cat = Categorical(no_index[df.index.levels[1].name])
d = get_dummies(cat, drop_first=False).astype(np.float64)
dummy_demeaned = df.values - d @ lstsq(d, df.values, rcond=None)[0]
time_demean = dh.demean("time")
assert_allclose(1 + np.abs(time_demean.values2d),
1 + np.abs(dummy_demeaned))
cat = Categorical(no_index[df.index.levels[0].name])
d1 = get_dummies(cat, drop_first=False).astype(np.float64)
cat = Categorical(no_index[df.index.levels[1].name])
d2 = get_dummies(cat, drop_first=True).astype(np.float64)
d = np.c_[d1.values, d2.values]
dummy_demeaned = df.values - d @ lstsq(d, df.values, rcond=None)[0]
both_demean = dh.demean("both")
assert_allclose(1 + np.abs(both_demean.values2d),
1 + np.abs(dummy_demeaned))
def test_demean_weighted(data):
x = PanelData(data.x)
w = PanelData(data.w)
missing = x.isnull | w.isnull
x.drop(missing)
w.drop(missing)
entity_demean = x.demean('entity', weights=w)
d = pd.get_dummies(pd.Categorical(x.index.labels[0]))
d = d.values
root_w = np.sqrt(w.values2d)
wx = root_w * x.values2d
wd = d * root_w
mu = wd @ np.linalg.lstsq(wd, wx)[0]
e = wx - mu
assert_allclose(1 + np.abs(entity_demean.values2d),
1 + np.abs(e))
time_demean = x.demean('time', weights=w)
d = pd.get_dummies(pd.Categorical(x.index.labels[1]))
d = d.values
root_w = np.sqrt(w.values2d)
wx = root_w * x.values2d
wd = d * root_w
mu = wd @ np.linalg.lstsq(wd, wx)[0]
e = wx - mu
assert_allclose(1 + np.abs(time_demean.values2d),
1 + np.abs(e))
def test_demean_many_missing(panel):
panel.iloc[0, ::3] = np.nan
panel.iloc[0, :, ::3] = np.nan
panel.iloc[1, ::5] = np.nan
panel.iloc[1, :, ::5] = np.nan
panel.iloc[2, ::2] = np.nan
panel.iloc[2, :, ::2] = np.nan
data = PanelData(panel)
fe = data.demean('entity')
orig_nan = np.isnan(panel.values.ravel())
fe_nan = np.isnan(fe.values3d.ravel())
assert np.all(fe_nan[orig_nan])
expected = panel.values.copy()
for i in range(3):
mu = np.ones(expected[i].shape[1]) * np.nan
for j in range(expected[i].shape[1]):
if np.any(np.isfinite(expected[i][:, j])):
mu[j] = np.nanmean(expected[i][:, j])
expected[i] -= mu
assert_allclose(fe.values3d, expected)
te = data.demean('time')
expected = panel.values.copy()
for i in range(3):
mu = np.ones((expected[i].shape[0], 1)) * np.nan
for j in range(expected[i].shape[0]):
if np.any(np.isfinite(expected[i][j])):
mu[j, 0] = np.nanmean(expected[i][j])
expected[i] -= mu
assert_allclose(te.values3d, expected)
def test_demean_weighted(data):
x = PanelData(data.x)
w = PanelData(data.w)
missing = x.isnull | w.isnull
x.drop(missing)
w.drop(missing)
entity_demean = x.demean("entity", weights=w)
d = get_dummies(Categorical(get_codes(x.index)[0]))
d = d.values
root_w = np.sqrt(w.values2d)
wx = root_w * x.values2d
wd = d * root_w
mu = wd @ lstsq(wd, wx, rcond=None)[0]
e = wx - mu
assert_allclose(1 + np.abs(entity_demean.values2d), 1 + np.abs(e))
time_demean = x.demean("time", weights=w)
d = get_dummies(Categorical(get_codes(x.index)[1]))
d = d.values
root_w = np.sqrt(w.values2d)
wx = root_w * x.values2d
wd = d * root_w
mu = wd @ lstsq(wd, wx, rcond=None)[0]
e = wx - mu
assert_allclose(1 + np.abs(time_demean.values2d), 1 + np.abs(e))
def test_general_unit_weighted_demean_oneway(mi_df):
y = PanelData(mi_df)
dm1 = y.demean("entity")
g = PanelData(DataFrame(y.entity_ids, index=y.index))
weights = PanelData(g).copy()
weights.dataframe.iloc[:, :] = 1
dm2 = y.general_demean(g, weights)
assert_allclose(dm1.values2d, dm2.values2d)
dm3 = y.general_demean(g)
assert_allclose(dm3.values2d, dm2.values2d)
dm1 = y.demean("time")
g = PanelData(DataFrame(y.time_ids, index=y.index))
dm2 = y.general_demean(g, weights)
assert_allclose(dm1.values2d, dm2.values2d)
dm3 = y.general_demean(g)
assert_allclose(dm3.values2d, dm2.values2d)
g = PanelData(
DataFrame(np.random.randint(0, 10, g.dataframe.shape), index=y.index))
dm2 = y.general_demean(g, weights)
dm3 = y.general_demean(g)
g = Categorical(g.dataframe.iloc[:, 0])
d = get_dummies(g)
dm1 = y.values2d - d @ lstsq(d, y.values2d, rcond=None)[0]
assert_allclose(dm1, dm2.values2d)
assert_allclose(dm3.values2d, dm2.values2d)
def test_general_weighted_demean_oneway(panel):
y = PanelData(panel)
weights = pd.DataFrame(np.random.chisquare(10,
(y.dataframe.shape[0], 1)) / 10,
index=y.index)
w = PanelData(weights)
dm1 = y.demean('entity', weights=w)
g = PanelData(pd.DataFrame(y.entity_ids, index=y.index))
dm2 = y.general_demean(g, w)
assert_allclose(dm1.values2d, dm2.values2d)
dm1 = y.demean('time', weights=w)
g = PanelData(pd.DataFrame(y.time_ids, index=y.index))
dm2 = y.general_demean(g, w)
assert_allclose(dm1.values2d, dm2.values2d)
g = PanelData(
pd.DataFrame(np.random.randint(0, 10, g.dataframe.shape),
index=y.index))
dm2 = y.general_demean(g, w)
g = pd.Categorical(g.dataframe.iloc[:, 0])
d = pd.get_dummies(g)
wd = np.sqrt(w.values2d) * d
wy = np.sqrt(w.values2d) * y.values2d
dm1 = wy - wd @ np.linalg.lstsq(wd, wy)[0]
assert_allclose(dm1, dm2.values2d, atol=1e-14)
def test_general_weighted_demean_oneway(mi_df):
y = PanelData(mi_df)
weights = DataFrame(np.random.chisquare(10,
(y.dataframe.shape[0], 1)) / 10,
index=y.index)
w = PanelData(weights)
dm1 = y.demean("entity", weights=w)
g = PanelData(DataFrame(y.entity_ids, index=y.index))
dm2 = y.general_demean(g, w)
assert_allclose(dm1.values2d, dm2.values2d)
dm1 = y.demean("time", weights=w)
g = PanelData(DataFrame(y.time_ids, index=y.index))
dm2 = y.general_demean(g, w)
assert_allclose(dm1.values2d, dm2.values2d)
g = PanelData(
DataFrame(np.random.randint(0, 10, g.dataframe.shape), index=y.index))
dm2 = y.general_demean(g, w)
g = Categorical(g.dataframe.iloc[:, 0])
d = get_dummies(g)
wd = np.sqrt(w.values2d) * d
wy = np.sqrt(w.values2d) * y.values2d
dm1 = wy - wd @ lstsq(wd, wy, rcond=None)[0]
assert_allclose(dm1, dm2.values2d, atol=1e-14)
def test_general_unit_weighted_demean_oneway(panel):
y = PanelData(panel)
dm1 = y.demean('entity')
g = PanelData(pd.DataFrame(y.entity_ids, index=y.index))
weights = PanelData(g).copy()
weights.dataframe.iloc[:, :] = 1
dm2 = y.general_demean(g, weights)
assert_allclose(dm1.values2d, dm2.values2d)
dm3 = y.general_demean(g)
assert_allclose(dm3.values2d, dm2.values2d)
dm1 = y.demean('time')
g = PanelData(pd.DataFrame(y.time_ids, index=y.index))
dm2 = y.general_demean(g, weights)
assert_allclose(dm1.values2d, dm2.values2d)
dm3 = y.general_demean(g)
assert_allclose(dm3.values2d, dm2.values2d)
g = PanelData(pd.DataFrame(np.random.randint(0, 10, g.dataframe.shape), index=y.index))
dm2 = y.general_demean(g, weights)
dm3 = y.general_demean(g)
g = pd.Categorical(g.dataframe.iloc[:, 0])
d = pd.get_dummies(g)
dm1 = y.values2d - d @ np.linalg.lstsq(d, y.values2d)[0]
assert_allclose(dm1, dm2.values2d)
assert_allclose(dm3.values2d, dm2.values2d)
def test_demean_missing_alt_types(data):
xpd = PanelData(data.x)
xpd.drop(xpd.isnull)
entity_demean = xpd.demean('entity')
expected = xpd.dataframe.groupby(level=0).transform(lambda s: s - s.mean())
assert_frame_equal(entity_demean.dataframe, expected)
time_demean = xpd.demean('time')
expected = xpd.dataframe.groupby(level=1).transform(lambda s: s - s.mean())
assert_frame_equal(time_demean.dataframe, expected)
def test_demean(panel):
data = PanelData(panel)
fe = data.demean('entity')
expected = panel.values.copy()
for i in range(3):
expected[i] -= expected[i].mean(0)
assert_allclose(fe.values3d, expected)
te = data.demean('time')
expected = panel.values.copy()
for i in range(3):
expected[i] -= expected[i].mean(1)[:, None]
assert_allclose(te.values3d, expected)
def test_demean(mi_df):
data = PanelData(mi_df)
fe = data.demean("entity")
expected = data.values3d.copy()
for i in range(3):
expected[i] -= expected[i].mean(0)
assert_allclose(fe.values3d, expected)
te = data.demean("time")
expected = data.values3d.copy()
for i in range(3):
expected[i] -= expected[i].mean(1)[:, None]
assert_allclose(te.values3d, expected)
def test_demean_against_groupby(data):
dh = PanelData(data.x)
df = dh.dataframe
def demean(x):
return x - x.mean()
entity_demean = df.groupby(level=0).transform(demean)
res = dh.demean("entity")
assert_allclose(entity_demean.values, res.values2d)
time_demean = df.groupby(level=1).transform(demean)
res = dh.demean("time")
assert_allclose(time_demean.values, res.values2d)
def test_demean_missing(mi_df):
mi_df.values.flat[::13] = np.nan
data = PanelData(mi_df)
fe = data.demean("entity")
expected = data.values3d.copy()
for i in range(3):
expected[i] -= np.nanmean(expected[i], 0)
assert_allclose(fe.values3d, expected)
te = data.demean("time")
expected = data.values3d.copy()
for i in range(3):
expected[i] -= np.nanmean(expected[i], 1)[:, None]
assert_allclose(te.values3d, expected)
def test_demean_simple_weighted(data):
x = PanelData(data.x)
w = PanelData(data.w)
missing = x.isnull | w.isnull
x.drop(missing)
w.drop(missing)
w.dataframe.iloc[:, 0] = 1
unweighted_entity_demean = x.demean('entity')
weighted_entity_demean = x.demean('entity', weights=w)
assert_allclose(unweighted_entity_demean.dataframe, weighted_entity_demean.dataframe)
unweighted_entity_demean = x.demean('time')
weighted_entity_demean = x.demean('time', weights=w)
assert_allclose(unweighted_entity_demean.dataframe, weighted_entity_demean.dataframe)
def test_demean_missing(panel):
panel.values.flat[::13] = np.nan
data = PanelData(panel)
fe = data.demean('entity')
expected = panel.values.copy()
for i in range(3):
expected[i] -= np.nanmean(expected[i], 0)
assert_allclose(fe.values3d, expected)
te = data.demean('time')
expected = panel.values.copy()
for i in range(3):
expected[i] -= np.nanmean(expected[i], 1)[:, None]
assert_allclose(te.values3d, expected)
def test_demean_missing_alt_types(data):
check = isinstance(data.x, (DataFrame, np.ndarray))
xpd = PanelData(data.x)
xpd.drop(xpd.isnull)
entity_demean = xpd.demean('entity')
expected = xpd.dataframe.groupby(level=0).transform(lambda s: s - s.mean())
assert_frame_equal(entity_demean.dataframe, expected,
check_index_type=check,
check_column_type=check)
time_demean = xpd.demean('time')
expected = xpd.dataframe.groupby(level=1).transform(lambda s: s - s.mean())
assert_frame_equal(time_demean.dataframe, expected,
check_index_type=check,
check_column_type=check)
def test_general_unit_weighted_demean_twoway(mi_df):
np.random.seed(12345)
y = PanelData(mi_df)
weights = DataFrame(np.random.chisquare(10,
(y.dataframe.shape[0], 1)) / 10,
index=y.index)
w = PanelData(weights)
dm1 = y.demean("both", weights=w)
g = DataFrame(y.entity_ids, index=y.index)
g["column2"] = Series(y.time_ids.squeeze(), index=y.index)
dm2 = y.general_demean(g, weights=w)
assert_allclose(dm1.values2d - dm2.values2d,
np.zeros_like(dm2.values2d),
atol=1e-7)
g = DataFrame(np.random.randint(0, 10, g.shape), index=y.index)
dm2 = y.general_demean(g, weights=w)
g1 = Categorical(g.iloc[:, 0])
d1 = get_dummies(g1)
g2 = Categorical(g.iloc[:, 1])
d2 = get_dummies(g2, drop_first=True)
d = np.c_[d1, d2]
wd = np.sqrt(w.values2d) * d
wy = np.sqrt(w.values2d) * y.values2d
dm1 = wy - wd @ lstsq(wd, wy, rcond=None)[0]
assert_allclose(dm1 - dm2.values2d, np.zeros_like(dm2.values2d), atol=1e-7)
def test_general_demean_oneway(panel):
y = PanelData(panel)
dm1 = y.demean('entity')
g = pd.DataFrame(y.entity_ids, index=y.index)
dm2 = y.general_demean(g)
assert_allclose(dm1.values2d, dm2.values2d)
dm1 = y.demean('time')
g = pd.DataFrame(y.time_ids, index=y.index)
dm2 = y.general_demean(g)
assert_allclose(dm1.values2d, dm2.values2d)
g = pd.DataFrame(np.random.randint(0, 10, g.shape), index=y.index)
dm2 = y.general_demean(g)
g = pd.Categorical(g.iloc[:, 0])
d = pd.get_dummies(g)
dm1 = y.values2d - d @ np.linalg.lstsq(d, y.values2d)[0]
assert_allclose(dm1, dm2.values2d)
def test_general_demean_oneway(mi_df):
y = PanelData(mi_df)
dm1 = y.demean("entity")
g = DataFrame(y.entity_ids, index=y.index)
dm2 = y.general_demean(g)
assert_allclose(dm1.values2d, dm2.values2d)
dm1 = y.demean("time")
g = DataFrame(y.time_ids, index=y.index)
dm2 = y.general_demean(g)
assert_allclose(dm1.values2d, dm2.values2d)
g = DataFrame(np.random.randint(0, 10, g.shape), index=y.index)
dm2 = y.general_demean(g)
g = Categorical(g.iloc[:, 0])
d = get_dummies(g)
dm1 = y.values2d - d @ lstsq(d, y.values2d, rcond=None)[0]
assert_allclose(dm1, dm2.values2d)
def test_demean_many_missing_dropped(panel):
panel.iloc[0, ::3, ::3] = np.nan
data = PanelData(panel)
data.drop(data.isnull)
fe = data.demean('entity')
expected = data.values2d.copy()
eid = data.entity_ids.ravel()
for i in np.unique(eid):
expected[eid == i] -= np.nanmean(expected[eid == i], 0)
assert_allclose(fe.values2d, expected)
def test_demean_both_large_t():
data = PanelData(pd.Panel(np.random.standard_normal((1, 100, 10))))
demeaned = data.demean('both')
df = data.dataframe
no_index = df.reset_index()
cat = pd.Categorical(no_index[df.index.levels[0].name])
d1 = pd.get_dummies(cat, drop_first=False).astype(np.float64)
cat = pd.Categorical(no_index[df.index.levels[1].name])
d2 = pd.get_dummies(cat, drop_first=True).astype(np.float64)
d = np.c_[d1.values, d2.values]
dummy_demeaned = df.values - d @ pinv(d) @ df.values
assert_allclose(1 + np.abs(demeaned.values2d), 1 + np.abs(dummy_demeaned))
def test_demean_many_missing(panel):
panel.iloc[0, ::3] = np.nan
panel.iloc[0, :, ::3] = np.nan
panel.iloc[1, ::5] = np.nan
panel.iloc[1, :, ::5] = np.nan
panel.iloc[2, ::2] = np.nan
panel.iloc[2, :, ::2] = np.nan
data = PanelData(panel)
fe = data.demean('entity')
orig_nan = np.isnan(panel.values.ravel())
fe_nan = np.isnan(fe.values3d.ravel())
assert np.all(fe_nan[orig_nan])
expected = panel.values.copy()
for i in range(3):
expected[i] -= np.nanmean(expected[i], 0)
assert_allclose(fe.values3d, expected)
te = data.demean('time')
expected = panel.values.copy()
for i in range(3):
expected[i] -= np.nanmean(expected[i], 1)[:, None]
assert_allclose(te.values3d, expected)
def test_general_demean_twoway(mi_df):
y = PanelData(mi_df)
dm1 = y.demean("both")
g = DataFrame(y.entity_ids, index=y.index)
g["column2"] = Series(y.time_ids.squeeze(), index=y.index)
dm2 = y.general_demean(g)
assert_allclose(dm1.values2d, dm2.values2d)
g = DataFrame(np.random.randint(0, 10, g.shape), index=y.index)
dm2 = y.general_demean(g)
g1 = Categorical(g.iloc[:, 0])
d1 = get_dummies(g1)
g2 = Categorical(g.iloc[:, 1])
d2 = get_dummies(g2, drop_first=True)
d = np.c_[d1, d2]
dm1 = y.values2d - d @ lstsq(d, y.values2d, rcond=None)[0]
assert_allclose(dm1 - dm2.values2d, np.zeros_like(dm2.values2d), atol=1e-7)
def test_demean_both_large_t():
x = np.random.standard_normal((1, 100, 10))
time = date_range("1-1-2000", periods=100)
entities = ["entity.{0}".format(i) for i in range(10)]
data = panel_to_frame(x, ["x"], time, entities, swap=True)
data = PanelData(data)
demeaned = data.demean("both")
df = data.dataframe
no_index = df.reset_index()
cat = Categorical(no_index[df.index.levels[0].name])
d1 = get_dummies(cat, drop_first=False).astype(np.float64)
cat = Categorical(no_index[df.index.levels[1].name])
d2 = get_dummies(cat, drop_first=True).astype(np.float64)
d = np.c_[d1.values, d2.values]
dummy_demeaned = df.values - d @ pinv(d) @ df.values
assert_allclose(1 + np.abs(demeaned.values2d), 1 + np.abs(dummy_demeaned))
def test_general_demean_twoway(panel):
y = PanelData(panel)
dm1 = y.demean('both')
g = pd.DataFrame(y.entity_ids, index=y.index)
g['column2'] = pd.Series(y.time_ids.squeeze(), index=y.index)
dm2 = y.general_demean(g)
assert_allclose(dm1.values2d, dm2.values2d)
g = pd.DataFrame(np.random.randint(0, 10, g.shape), index=y.index)
dm2 = y.general_demean(g)
g1 = pd.Categorical(g.iloc[:, 0])
d1 = pd.get_dummies(g1)
g2 = pd.Categorical(g.iloc[:, 1])
d2 = pd.get_dummies(g2, drop_first=True)
d = np.c_[d1, d2]
dm1 = y.values2d - d @ np.linalg.lstsq(d, y.values2d)[0]
assert_allclose(dm1 - dm2.values2d, np.zeros_like(dm2.values2d), atol=1e-7)
def test_demean_many_missing_dropped(mi_df):
entities = mi_df.index.levels[0]
times = mi_df.index.levels[1]
column = mi_df.columns[0]
for entity in entities[::3]:
mi_df.loc[entity, column] = np.nan
mi_df.index = mi_df.index.swaplevel()
for time in times[::3]:
mi_df.loc[time, column] = np.nan
mi_df.index = mi_df.index.swaplevel()
data = PanelData(mi_df)
data.drop(data.isnull)
fe = data.demean("entity")
expected = data.values2d.copy()
eid = data.entity_ids.ravel()
for i in np.unique(eid):
expected[eid == i] -= np.nanmean(expected[eid == i], 0)
assert_allclose(fe.values2d, expected)
mod = PanelOLS(data.y, data.x, weights=data.w)
mod.fit()
mod = PanelOLS(y, x, weights=data.w, entity_effects=True)
mod.fit()
mod = PanelOLS(data.y, data.x, weights=data.w, time_effects=True)
mod.fit()
mod = PanelOLS(data.y, data.x, weights=data.w, time_effects=True, entity_effects=True)
mod.fit()
missing = y.isnull | x.isnull | w.isnull
y.drop(missing)
x.drop(missing)
w.drop(missing)
x.dataframe.iloc[:, 0] = 1
ydw = y.demean(weights=w)
xdw = x.demean(weights=w)
d = x.dummies('entity', drop_first=False)
root_w = np.sqrt(w.values2d)
wd = root_w * d
wdx_direct = root_w * x.values2d - wd @ np.linalg.lstsq(wd, root_w * x.values2d)[0]
print(np.abs(wdx_direct[0] - xdw.values2d[0]) > 1e-14)
mux = (w.values2d * x.values2d).sum(0) / w.values2d.sum()
muy = (w.values2d * y.values2d).sum(0) / w.values2d.sum()
xx = xdw.values2d + root_w * mux
yy = ydw.values2d + root_w * muy.squeeze()
print(np.linalg.lstsq(xx, yy)[0])
yyy = root_w * y.values2d
xxx = root_w * x.values2d
def test_demean_invalid(mi_df):
data = PanelData(mi_df)
with pytest.raises(ValueError):
data.demean("unknown")
def test_demean_invalid(panel):
data = PanelData(panel)
with pytest.raises(ValueError):
data.demean('unknown')
