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bashtage merged 1 commit into from
Sep 24, 2026
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TST: actually seed TestForecasting fixtures #865

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21 changes: 20 additions & 1 deletion arch/tests/univariate/test_forecast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -96,28 +96,42 @@ def analytical_model_spec(request):
class TestForecasting:
@classmethod
def setup_class(cls):
# `arch_model(...)`'s `dist` argument only accepts a distribution
# *name* (see `arch_model`'s signature) - it always builds its own
# Normal() with an unseeded `default_rng()`, so `cls.rng` here never
# actually reached the simulator despite looking like it seeds one.
# Assigning onto `.distribution` (a public, settable property) after
# construction is what wires the seed in; reusing the same
# RandomState across all five simulate() calls below keeps their
# outputs one reproducible stream, matching what the shared `cls.rng`
# was clearly meant to do.
cls.rng = RandomState(12345)
am = arch_model(None, mean="Constant", vol="Constant")
am.distribution = Normal(seed=cls.rng)
data = am.simulate(np.array([0.0, 10.0]), 1000)
data.index = pd.date_range("2000-01-01", periods=data.index.shape[0])
cls.zero_mean = data.data

am = arch_model(None, mean="AR", vol="Constant", lags=[1])
am.distribution = Normal(seed=cls.rng)
data = am.simulate(np.array([1.0, 0.9, 2]), 1000)
data.index = pd.date_range("2000-01-01", periods=data.index.shape[0])
cls.ar1 = data.data

am = arch_model(None, mean="AR", vol="Constant", lags=[1, 2])
am.distribution = Normal(seed=cls.rng)
data = am.simulate(np.array([1.0, 1.9, -0.95, 2]), 1000)
data.index = pd.date_range("2000-01-01", periods=data.index.shape[0])
cls.ar2 = data.data

am = arch_model(None, mean="HAR", vol="Constant", lags=[1, 5, 22])
am.distribution = Normal(seed=cls.rng)
data = am.simulate(np.array([1.0, 0.4, 0.3, 0.2, 2]), 1000)
data.index = pd.date_range("2000-01-01", periods=data.index.shape[0])
cls.har3 = data.data

am = arch_model(None, mean="AR", vol="GARCH", lags=[1, 2], p=1, q=1)
am.distribution = Normal(seed=cls.rng)
data = am.simulate(np.array([1.0, 1.9, -0.95, 0.05, 0.1, 0.88]), 1000)
data.index = pd.date_range("2000-01-01", periods=data.index.shape[0])
cls.ar2_garch = data.data
Expand Down Expand Up @@ -548,7 +562,12 @@ def test_first_obs(self):
res = mod.fit(disp="off", first_obs=y.index[100])
mod = arch_model(y[100:])
res2 = mod.fit(disp="off")
assert_allclose(res.params, res2.params)
# These two fits solve numerically distinct optimizations (different
# data views feed the same nonconvex GARCH likelihood surface), so
# only the same rtol test_holdback already uses for the equivalent
# comparison above is realistic - the optimizer's exact landing point
# is legitimately scipy-version-sensitive even when both converge.
assert_allclose(res.params, res2.params, rtol=1e-4, atol=1e-4)
mod = arch_model(y)
res3 = mod.fit(disp="off", first_obs=100)
assert res.fit_start == 100
Expand Down