def res_opg(self):
res_ols = self.res_ols
nobs = self.nobs
moms = self.moms
moms_obs = self.moms_obs
covm = self.covm
moms_deriv = self.moms_deriv
weights = self.weights
L = self.L
x = self.exog_full
res_ols2_hc0 = OLS(res_ols.model.endog, x).fit(cov_type='HC0')
res_all = []
# auxiliary regression
ones = np.ones(nobs)
stat = nobs * OLS(ones, moms_obs).fit().rsquared
res_all.append(('ols R2', stat))
tres = res_ols2_hc0.compare_lm_test(res_ols, demean=False)
res_all.append(('comp_lm uc', tres))
tres = CMTNewey(moms, covm, covm[:, :-2], weights, L).chisquare
res_all.append(('Newey', tres))
tres = CMTTauchen(moms[:-2], covm[:-2, :-2], moms[-2:], covm[-2:, :-2],
covm).chisquare
res_all.append(('Tauchen', tres))
tres = diao.lm_robust_subset(moms[-2:], 2, covm, covm)
res_all.append(('score subset QMLE', tres))
tres = diao.lm_robust(moms,
np.eye(moms.shape[0])[-2:],
np.linalg.inv(covm),
covm,
cov_params=None)
res_all.append(('scoreB QMLE', tres))
tres = diao.lm_robust(moms,
np.eye(moms.shape[0])[-2:],
np.linalg.inv(covm),
None,
cov_params=np.linalg.inv(covm))
res_all.append(('scoreV QMLE', tres))
return res_all
def res_opg(self):
res_ols = self.res_ols
nobs = self.nobs
moms = self.moms
moms_obs = self.moms_obs
covm = self.covm
moms_deriv = self.moms_deriv
weights = self.weights
L = self.L
x = self.exog_full
res_ols2_hc0 = OLS(res_ols.model.endog, x).fit(cov_type='HC0')
res_all = []
# auxiliary regression
ones = np.ones(nobs)
stat = nobs * OLS(ones, moms_obs).fit().rsquared
res_all.append(('ols R2', stat))
tres = res_ols2_hc0.compare_lm_test(res_ols, demean=False)
res_all.append(('comp_lm uc', tres))
tres = CMTNewey(moms, covm, covm[:,:-2], weights, L).chisquare
res_all.append(('Newey', tres))
tres = CMTTauchen(moms[:-2], covm[:-2, :-2], moms[-2:], covm[-2:, :-2],
covm).chisquare
res_all.append(('Tauchen', tres))
tres = diao.lm_robust_subset(moms[-2:], 2, covm, covm)
res_all.append(('score subset QMLE', tres))
tres = diao.lm_robust(moms, np.eye(moms.shape[0])[-2:],
np.linalg.inv(covm), covm, cov_params=None)
res_all.append(('scoreB QMLE', tres))
tres = diao.lm_robust(moms, np.eye(moms.shape[0])[-2:],
np.linalg.inv(covm), None,
cov_params=np.linalg.inv(covm))
res_all.append(('scoreV QMLE', tres))
return res_all
def res_hc0(self):
res_ols = self.res_ols
nobs = self.nobs
moms = self.moms
moms_obs = self.moms_obs
cov_moms = self.cov_moms # Hessian with scale
covm = self.covm
moms_deriv = self.moms_deriv
weights = self.weights
L = self.L
x0 = res_ols.model.exog
x1 = self.exog_add
res_all = []
tres = diao.cm_test_robust(resid=res_ols.resid,
resid_deriv=x0,
instruments=x1,
weights=1)
# TODO: extra return and no df in cm_test_robust Wooldridge
res_all.append(('Wooldridge', tres[:2]))
tres = CMTNewey(moms, covm, moms_deriv, weights, L).chisquare
res_all.append(('Newey', tres))
tres = CMTTauchen(moms[:-2], cov_moms[:-2, :-2], moms[-2:],
cov_moms[-2:, :-2], covm).chisquare
res_all.append(('Tauchen', tres))
tres = diao.lm_robust_subset(moms[-2:], 2, cov_moms, covm)
res_all.append(('score subset QMLE', tres))
tres = diao.lm_robust(moms,
np.eye(moms.shape[0])[-2:],
np.linalg.inv(cov_moms), covm)
res_all.append(('scoreB QMLE', tres))
# need sandwich cov_params V
Ainv = np.linalg.inv(cov_moms)
vv = Ainv.dot(covm).dot(Ainv)
tres = diao.lm_robust(moms,
np.eye(moms.shape[0])[-2:],
np.linalg.inv(cov_moms),
None,
cov_params=vv)
res_all.append(('scoreV QMLE', tres))
tres = diao.conditional_moment_test_generic(moms_obs[:, -2:],
cov_moms[-2:, :-2],
moms_obs[:, :-2],
cov_moms[:-2, :-2])
tres_ = (tres.stat_cmt, tres.pval_cmt)
res_all.append(('cmt', tres_))
# using unscaled hessian instead of scaled
x = self.exog_full
hess_unscaled = x.T.dot(x)
tres = diao.conditional_moment_test_generic(
moms_obs[:, -2:], hess_unscaled[-2:, :-2], moms_obs[:, :-2],
hess_unscaled[:-2, :-2]) #, covm)
tres_ = (tres.stat_cmt, tres.pval_cmt)
res_all.append(('cmt', tres_))
score_deriv_uu = cov_moms[:-2, :-2]
score_deriv_cu = cov_moms[-2:, :-2]
cov_score_cc = covm[-2:, -2:]
cov_score_cu = covm[-2:, :-2]
cov_score_uu = covm[:-2, :-2]
moms[-2:], 2, cov_moms, covm
tres = diao.lm_robust_subset_parts(moms[-2:], 2, score_deriv_uu,
score_deriv_cu, cov_score_cc,
cov_score_cu, cov_score_uu)
res_all.append(('score subset_parts QMLE', tres))
params_deriv = np.eye(x.shape[1], x.shape[1] - 2)
#params_deriv[[-2, -1], [-2, -1]] = 0
score = moms
score_deriv = cov_moms
cov_score = covm
tres = diao.lm_robust_reparameterized(score, params_deriv, score_deriv,
cov_score)
res_all.append(('score reparam QMLE', tres))
return res_all
def res_hc0(self):
res_ols = self.res_ols
nobs = self.nobs
moms = self.moms
moms_obs = self.moms_obs
cov_moms = self.cov_moms # Hessian with scale
covm = self.covm
moms_deriv = self.moms_deriv
weights = self.weights
L = self.L
x0 = res_ols.model.exog
x1 = self.exog_add
res_all = []
tres = diao.cm_test_robust(resid=res_ols.resid, resid_deriv=x0,
instruments=x1, weights=1)
# TODO: extra return and no df in cm_test_robust Wooldridge
res_all.append(('Wooldridge', tres[:2]))
tres = CMTNewey(moms, covm, moms_deriv, weights, L).chisquare
res_all.append(('Newey', tres))
tres = CMTTauchen(moms[:-2], cov_moms[:-2, :-2], moms[-2:],
cov_moms[-2:, :-2], covm).chisquare
res_all.append(('Tauchen', tres))
tres = diao.lm_robust_subset(moms[-2:], 2, cov_moms, covm)
res_all.append(('score subset QMLE', tres))
tres = diao.lm_robust(moms, np.eye(moms.shape[0])[-2:],
np.linalg.inv(cov_moms), covm)
res_all.append(('scoreB QMLE', tres))
# need sandwich cov_params V
Ainv = np.linalg.inv(cov_moms)
vv = Ainv.dot(covm).dot(Ainv)
tres = diao.lm_robust(moms, np.eye(moms.shape[0])[-2:],
np.linalg.inv(cov_moms), None,
cov_params=vv)
res_all.append(('scoreV QMLE', tres))
tres = diao.conditional_moment_test_generic(moms_obs[:, -2:],
cov_moms[-2:, :-2],
moms_obs[:,:-2],
cov_moms[:-2, :-2])
tres_ = (tres.stat_cmt, tres.pval_cmt)
res_all.append(('cmt', tres_))
# using unscaled hessian instead of scaled
x = self.exog_full
hess_unscaled = x.T.dot(x)
tres = diao.conditional_moment_test_generic(moms_obs[:, -2:],
hess_unscaled[-2:, :-2], moms_obs[:,:-2],
hess_unscaled[:-2, :-2])#, covm)
tres_ = (tres.stat_cmt, tres.pval_cmt)
res_all.append(('cmt', tres_))
score_deriv_uu = cov_moms[:-2, :-2]
score_deriv_cu = cov_moms[-2:, :-2]
cov_score_cc = covm[-2:, -2:]
cov_score_cu = covm[-2:, :-2]
cov_score_uu = covm[:-2, :-2]
moms[-2:], 2, cov_moms, covm
tres = diao.lm_robust_subset_parts(moms[-2:], 2, score_deriv_uu,
score_deriv_cu, cov_score_cc,
cov_score_cu, cov_score_uu)
res_all.append(('score subset_parts QMLE', tres))
params_deriv = np.eye(x.shape[1], x.shape[1] - 2)
#params_deriv[[-2, -1], [-2, -1]] = 0
score = moms
score_deriv = cov_moms
cov_score = covm
tres = diao.lm_robust_reparameterized(score, params_deriv,
score_deriv, cov_score)
res_all.append(('score reparam QMLE', tres))
return res_all