def test_incorrect_type(data):
dependent = data.set_index(['nr', 'year']).lwage
exog = sm.add_constant(
data.set_index(['nr', 'year'])[['expersq', 'married', 'union']])
mod = PanelOLS(dependent, exog)
res = mod.fit()
mod2 = IV2SLS(mod.dependent.dataframe, mod.exog.dataframe, None, None)
res2 = mod2.fit()
with pytest.raises(TypeError):
compare(dict(model1=res, model2=res2))
def test_incorrect_type(data):
dependent = data.set_index(["nr", "year"]).lwage
exog = add_constant(
data.set_index(["nr", "year"])[["expersq", "married", "union"]])
mod = PanelOLS(dependent, exog)
res = mod.fit()
mod2 = IV2SLS(mod.dependent.dataframe, mod.exog.dataframe, None, None)
res2 = mod2.fit()
with pytest.raises(TypeError):
compare(dict(model1=res, model2=res2))
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_single(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).fit()
comp = compare([res])
assert len(comp.rsquared) == 1
d = dir(comp)
for value in d:
if value.startswith("_"):
continue
getattr(comp, value)
def test_single(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).fit()
comp = compare([res])
assert len(comp.rsquared) == 1
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_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')
mod = PanelOLS(dependent, exog, entity_effects=True, time_effects=True) res = mod.fit(cov_type='unadjusted') res2 = mod.fit(cov_type='robust') exog = sm.add_constant(data[['exper', 'expersq','married','union']]) mod = PanelOLS(dependent, exog, entity_effects=True) res3 = mod.fit(cov_type='clustered',cluster_entity=True) mod = RandomEffects(dependent, exog) res4 = mod.fit(cov_type='robust') from linearmodels.panel.results import compare exog = sm.add_constant(data[['exper', 'expersq','married','union']].copy()) import pandas as pd exog['year'] = pd.Categorical(data.reset_index()['year']) mod = PooledOLS(dependent, exog) res5 = mod.fit(cov_type='robust') print(compare([res,res2, res3, res4, res5])) print(data.columns) 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']])
