def test_fitted_predict(data, model):
mod = model(data.dep, None, data.endog, data.instr)
res = mod.fit()
assert_series_equal(res.idiosyncratic, res.resids)
y = mod.dependent.pandas
expected = y.values - res.resids.values[:, None]
expected = DataFrame(expected, y.index, ['fitted_values'])
assert_frame_similar(expected, res.fitted_values)
assert_allclose(expected, res.fitted_values)
pred = res.predict()
nobs = res.resids.shape[0]
assert isinstance(pred, DataFrame)
assert pred.shape == (nobs, 1)
pred = res.predict(idiosyncratic=True, missing=True)
nobs = IVData(data.dep).pandas.shape[0]
assert pred.shape == (nobs, 2)
assert list(pred.columns) == ['fitted_values', 'residual']
def test_fitted_effects_residuals(both_data_types):
mod = BetweenOLS(both_data_types.y, both_data_types.x)
res = mod.fit(reweight=True, debiased=False)
expected = pd.DataFrame(
mod.exog.values2d @ res.params.values,
mod.dependent.index,
columns=["fitted_values"],
)
assert_allclose(expected, res.fitted_values)
assert_frame_similar(res.fitted_values, expected)
index = mod.dependent.dataframe.index
reindex = index.levels[0][get_codes(index)[0]]
resids = res.resids.copy()
resids = resids.reindex(reindex)
resids.index = index
expected = pd.DataFrame(resids)
expected.columns = ["estimated_effects"]
assert_allclose(expected, res.estimated_effects)
assert_frame_similar(res.estimated_effects, expected)
fitted_effects = res.fitted_values.values + res.estimated_effects.values
expected.iloc[:, 0] = mod.dependent.values2d - fitted_effects
expected.columns = ["idiosyncratic"]
assert_allclose(expected, res.idiosyncratic, atol=1e-8)
assert_frame_similar(res.idiosyncratic, expected)
def test_fitted_effects_residuals(data):
mod = PooledOLS(data.y, data.x)
res = mod.fit()
expected = pd.DataFrame(res.resids.copy())
expected.columns = ['idiosyncratic']
assert_allclose(res.idiosyncratic, expected)
assert_frame_similar(res.idiosyncratic, expected)
expected = mod.dependent.values2d - res.resids.values[:, None]
expected = pd.DataFrame(expected, index=res.resids.index, columns=['fitted_values'])
assert_allclose(res.fitted_values, expected)
assert_frame_similar(res.fitted_values, expected)
expected.iloc[:, 0] = np.nan
expected.columns = ['estimated_effects']
assert_allclose(res.estimated_effects, expected)
assert_frame_similar(res.estimated_effects, expected)
def test_fitted_effects_residuals(data):
mod = FirstDifferenceOLS(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] = mod.dependent.values2d - expected.values
expected.columns = ['idiosyncratic']
assert_allclose(res.idiosyncratic, expected)
assert_frame_similar(res.idiosyncratic, expected)
expected.iloc[:, 0] = np.nan
expected.columns = ['estimated_effects']
assert_allclose(res.estimated_effects, expected)
assert_frame_similar(res.estimated_effects, expected)
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_fitted_effects_residuals(data, entity_eff, time_eff):
mod = PanelOLS(data.y, data.x,
entity_effects=entity_eff,
time_effects=entity_eff)
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, atol=1e-8)
assert_frame_similar(res.estimated_effects, expected)
