def test_results_access(data):
mod = RandomEffects(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_fitted_effects_residuals(data):
mod = RandomEffects(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] = res.resids
expected.columns = ["idiosyncratic"]
assert_allclose(res.idiosyncratic, expected)
assert_frame_similar(res.idiosyncratic, expected)
fitted_error = res.fitted_values + res.idiosyncratic.values
expected.iloc[:, 0] = mod.dependent.values2d - fitted_error
expected.columns = ["estimated_effects"]
assert_allclose(res.estimated_effects, expected)
assert_frame_similar(res.estimated_effects, expected)
def test_random_effects_small_sample(data):
y = PanelData(data.y)
mod = RandomEffects(data.y, data.x)
no_ss = mod.fit()
ss = mod.fit(small_sample=True)
if y.dataframe.shape[0] == mod.dependent.dataframe.shape[0]:
assert ss.variance_decomposition.Effects == no_ss.variance_decomposition.Effects
else:
assert ss.variance_decomposition.Effects != no_ss.variance_decomposition.Effects
mod = RandomEffects(data.y, data.x, weights=data.w)
no_ss = mod.fit()
ss = mod.fit(small_sample=True)
if y.dataframe.shape[0] == mod.dependent.dataframe.shape[0]:
assert ss.variance_decomposition.Effects == no_ss.variance_decomposition.Effects
else:
assert ss.variance_decomposition.Effects != no_ss.variance_decomposition.Effects
def test_multiple_no_effects(data):
dependent = data.set_index(["nr", "year"]).lwage
exog = add_constant(
data.set_index(["nr", "year"])[["expersq", "married", "union"]])
res = PanelOLS(dependent, exog).fit()
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(dict(a=res, model2=res3, model3=res4))
assert len(comp.rsquared) == 3
d = dir(comp)
for value in d:
if value.startswith("_"):
continue
getattr(comp, value)
compare(dict(a=res, model2=res3, model3=res4))
def test_multiple_no_effects(data):
dependent = data.set_index(['nr', 'year']).lwage
exog = sm.add_constant(
data.set_index(['nr', 'year'])[['expersq', 'married', 'union']])
res = PanelOLS(dependent, exog).fit()
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(dict(a=res, model2=res3, model3=res4))
assert len(comp.rsquared) == 3
d = dir(comp)
for value in d:
if value.startswith('_'):
continue
getattr(comp, value)
compare(OrderedDict(a=res, model2=res3, model3=res4))
def test_multiple(data):
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])
assert len(comp.rsquared) == 4
d = dir(comp)
for value in d:
if value.startswith('_'):
continue
getattr(comp, value)
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_results_access(data):
mod = RandomEffects(data.y, data.x)
res = mod.fit(debiased=False)
access_attributes(res)
from linearmodels.panel.data import PanelData
import numpy as np
import pandas as pd
from statsmodels.datasets import grunfeld
from linearmodels.panel.model import RandomEffects
from linearmodels.tests.panel._utility import generate_data
data = pd.read_stata(
r'C:\git\linearmodels\linearmodels\tests\panel\results\simulated-panel.dta'
)
data = data.set_index(['firm', 'time'])
mod = RandomEffects(data.y, data[['intercept', 'x1', 'x2', 'x3', 'x4', 'x5']])
res = mod.fit()
print(res.variance_decomposition)
res = mod.fit(small_sample=True)
print(res.variance_decomposition)
raise NotImplementedError
mod = RandomEffects(
data.y_light, data[[
'intercept', 'x1_light', 'x2_light', 'x3_light', 'x4_light', 'x5_light'
]])
res = mod.fit()
import numpy as np
from statsmodels.datasets import grunfeld
data = grunfeld.load_pandas().data
data.year = data.year.astype(np.int64)
from linearmodels import PanelOLS
