code: def test_basic(self):
      res = self.res
      tt = res.t_test(np.eye(res.params.size))
      assert_allclose(
          tt.effect, res.params.to_numpy().ravel(order="F"), rtol=1e-13
      )
      assert_allclose(
          tt.sd, res.bse.to_numpy().ravel(order="F"), rtol=1e-13
      )
      assert_allclose(
          tt.tvalue, res.tvalues.to_numpy().ravel(order="F"), rtol=1e-13
      )
      assert_allclose(
          tt.pvalue, res.pvalues.to_numpy().ravel(order="F"), rtol=1e-13
      )
      assert_allclose(
          tt.conf_int(), res.conf_int().to_numpy(), rtol=1e-13
      )
  
      exog_names = res.model.exog_names
      endog_names = res.model.endog_names
  
      # hypothesis test for one parameter:
      for xn in exog_names:
          cnx = []
          for yn in endog_names:
              cn = f"y{yn}_{xn}"
              tt = res.t_test(cn)
              mvt = res.mv_test(hypotheses=[("one", [xn], [yn])])
              assert_allclose(
                  tt.pvalue,
                  mvt.summary_frame["Pr > F"].iloc[0],
                  rtol=1e-10,
              )
              cnx.append(cn)
  
          # wald test effect of an exog is zero across all endog
          # test methods are only asymptotically equivalent
          tt = res.wald_test(cnx, scalar=True)
          mvt = res.mv_test(hypotheses=[(xn, [xn], endog_names)])
          assert_allclose(
              tt.pvalue,
              mvt.summary_frame["Pr > F"].iloc[0],
              rtol=0.1,
              atol=1e-20,
          )
  
imports: import os.path
  import numpy
  import pandas
  import pytest
  from statsmodels.regression.linear_model import OLS
  from statsmodels.multivariate.multivariate_ols import _MultivariateOLS, MultivariateLS
  from numpy.testing import assert_allclose, assert_array_almost_equal, assert_raises
  import patsy
label_desc: Use image node_id 1 for calling the CheckMVConsistent obj's underlying member method code with example usage: obj.test_basic() without return types