test_covariance.py 文件源码

def test_homoskedastic_direct(cov_data, debias):
    x, z, eps, sigma = cov_data
    cov = HomoskedasticCovariance(x, eps, sigma, sigma, debiased=debias)
    k = len(x)
    nobs = x[0].shape[0]
    big_x = []
    for i in range(k):
        row = []
        for j in range(k):
            if i == j:
                row.append(x[i])
            else:
                row.append(np.zeros((nobs, x[j].shape[1])))
        big_x.append(np.concatenate(row, 1))
    big_x = np.concatenate(big_x, 0)
    xeex = big_x.T @ np.kron(sigma, np.eye(nobs)) @ big_x / nobs
    xpxi = _xpxi(x)
    direct = xpxi @ xeex @ xpxi / nobs
    direct = (direct + direct.T) / 2
    assert_allclose(np.diag(direct), np.diag(cov.cov))
    s = np.sqrt(np.diag(direct))[:, None]
    r_direct = direct / (s @ s.T)
    s = np.sqrt(np.diag(cov.cov))[:, None]
    c_direct = direct / (s @ s.T)
    assert_allclose(r_direct, c_direct, atol=1e-5)