def statsmodels_fixture():
"""These fixtures are taken from the statsmodels test battery and adapted towards
a random test."""
fix = {}
num_obs = 100
num_params = 3
max_range = 10
x = np.linspace(0, max_range, num_obs)
x = sm.add_constant(np.column_stack((x, x**2)), prepend=False)
beta = np.random.rand(num_params) * max_range
y = np.dot(x, beta) + np.random.normal(size=num_obs)
results = sm.OLS(y, x).fit()
fix["stats_cov"] = results.cov_HC0
params_df = pd.DataFrame({"value": results.params})
moment_cond = np.zeros((num_obs, num_params))
moment_jac = np.zeros((num_obs, num_params, num_params))
for i in range(num_obs):
moment_cond[i, :] = calc_moment_condition(params_df, x[i, :], y[i])
moment_jac[i, :, :] = jacobian(calc_moment_condition,
params_df,
func_kwargs={
"x_t": x[i, :],
"y_t": y[i]
})
fix["mom_cond"] = moment_cond
fix["mom_cond_jacob"] = moment_jac
fix["weighting_matrix"] = np.eye(num_params)
return fix
def np_jac(log_like_obs, params, method, log_like_kwargs):
"""We wrote this function because we did not want to touch the
differentiation files.
"""
numpy_jacobian = jacobian(log_like_obs,
params,
method,
func_kwargs=log_like_kwargs)
return numpy_jacobian.to_numpy()
def test_jacobian(statsmodels_fixtures, method, extrapolation):
fix = statsmodels_fixtures
func_kwargs = {"y": fix["y"], "x": fix["x"]}
calculated = jacobian(
logit_loglikeobs,
params=fix["params"],
method=method,
extrapolation=extrapolation,
func_kwargs=func_kwargs,
)
expected = fix["jacobian"]
assert_frame_equal(calculated, expected)