def test_panel_ols_formulas_math_op(data):
if not isinstance(data.y, DataFrame):
return
joined = data.x
joined["y"] = data.y
formula = "y ~ x1 + np.exp(x2)"
mod = PanelOLS.from_formula(formula, joined)
mod.fit()
def test_repeated_measures_weight():
# Issue reported by email
rs = np.random.RandomState(0)
w = rs.chisquare(5, 300) / 5
idx1 = ["a"] * 100 + ["b"] * 100 + ["c"] * 100
idx2 = np.arange(300) % 25
mi = pd.MultiIndex.from_arrays([idx1, idx2])
df = pd.DataFrame(rs.standard_normal((300, 2)),
index=mi,
columns=["y", "x"])
w = pd.Series(w, index=mi, name="weight")
df["weight"] = w
mod = PanelOLS.from_formula("y ~ x + EntityEffects + TimeEffects",
df,
weights=df["weight"])
res = mod.fit()
mod = PanelOLS.from_formula("y ~ x + EntityEffects + TimeEffects", df)
res_un = mod.fit()
assert res.params[0] != res_un.params[0]
def test_panel_ols_formula(data):
if not isinstance(data.y, DataFrame):
return
joined = data.x
joined["y"] = data.y
formula = "y ~ x1 + x2"
mod = PanelOLS.from_formula(formula, joined)
assert mod.formula == formula
formula = "y ~ x1 + x2 + EntityEffects"
mod = PanelOLS.from_formula(formula, joined)
assert mod.formula == formula
assert mod.entity_effects is True
assert mod.time_effects is False
formula = "y ~ x1 + x2 + TimeEffects"
mod = PanelOLS.from_formula(formula, joined)
assert mod.formula == formula
assert mod.time_effects is True
assert mod.entity_effects is False
formula = "y ~ x1 + EntityEffects + TimeEffects + x2 "
mod = PanelOLS.from_formula(formula, joined)
assert mod.formula == formula
assert mod.entity_effects is True
assert mod.time_effects is True
mod2 = panel_ols(formula, joined)
res = mod.fit()
res2 = mod2.fit()
np.testing.assert_allclose(res.params, res2.params)
formula = "y ~ x1 + EntityEffects + FixedEffects + x2 "
with pytest.raises(ValueError):
PanelOLS.from_formula(formula, joined)
