def test_against_direct_model(data):
keys = list(data.keys())
if not isinstance(data[keys[0]], Mapping):
return
if 'weights' in data[keys[0]]:
return
y = []
x = []
data_copy = OrderedDict()
for i in range(min(3, len(data))):
data_copy[keys[i]] = data[keys[i]]
y.append(data[keys[i]]['dependent'])
x.append(data[keys[i]]['exog'])
direct = simple_sur(y, x)
mod = SUR(data_copy)
res = mod.fit(method='ols')
assert_allclose(res.params.values[:, None], direct.beta0)
res = mod.fit(method='gls')
assert_allclose(res.params.values[:, None], direct.beta1)
def test_against_direct_model(data):
keys = list(data.keys())
if not isinstance(data[keys[0]], Mapping):
return
if "weights" in data[keys[0]]:
return
y = []
x = []
data_copy = {}
for i in range(min(3, len(data))):
data_copy[keys[i]] = data[keys[i]]
y.append(data[keys[i]]["dependent"])
x.append(data[keys[i]]["exog"])
direct = simple_sur(y, x)
mod = SUR(data_copy)
res = mod.fit(method="ols")
assert_allclose(res.params.values[:, None], direct.beta0)
res = mod.fit(method="gls")
assert_allclose(res.params.values[:, None], direct.beta1)