def setup_class(self):
# replicating OLS by GMM - high agreement
self.params_tol = [1e-11, 1e-12]
self.bse_tol = [1e-13, 1e-13]
exog = exog_st # with const at end
res_ols = OLS(endog, exog).fit()
#Note: start is irrelevant but required
start = np.ones(len(res_ols.params))
nobs, k_instr = instrument.shape
w0inv = np.dot(exog.T, exog) / nobs
#w0 = np.linalg.inv(w0inv)
mod = gmm.LinearIVGMM(endog, exog, exog)
res = mod.fit(
start,
maxiter=0,
inv_weights=w0inv,
#optim_method='bfgs', optim_args={'gtol':1e-6},
weights_method='iid',
wargs={
'centered': False,
'ddof': 'k_params'
},
has_optimal_weights=True)
self.res1 = res
#from results_gmm_griliches import results_onestep as results
#self.res2 = results
self.res2 = res_ols
def setup_class(cls):
# replicating OLS by GMM - high agreement
cls.params_tol = [1e-11, 1e-12]
cls.bse_tol = [1e-12, 1e-12]
exog = exog_st # with const at end
res_ols = OLS(endog, exog).fit()
#Note: start is irrelevant but required
start = np.ones(len(res_ols.params))
nobs, k_instr = instrument.shape
w0inv = np.dot(exog.T, exog) / nobs
#w0 = np.linalg.inv(w0inv)
mod = gmm.LinearIVGMM(endog, exog, exog)
res = mod.fit(
start,
maxiter=0,
inv_weights=w0inv,
#optim_method='bfgs', optim_args={'gtol':1e-6, 'disp': 0},
optim_args={'disp': 0},
weights_method='iid',
wargs={
'centered': False,
'ddof': 'k_params'
},
has_optimal_weights=True)
# fix use of t distribution see #2495 comment
res.use_t = True
res.df_resid = res.nobs - len(res.params)
cls.res1 = res
#from .results_gmm_griliches import results_onestep as results
#cls.res2 = results
cls.res2 = res_ols
def setup_class(self):
# compare to Stata default options, onestep GMM
# this uses maxiter=1, one iteration in loop
self.params_tol = [5e-9, 1e-9]
self.bse_tol = [5e-10, 1e-10]
exog = exog_st # with const at end
start = OLS(endog, exog).fit().params
nobs, k_instr = instrument.shape
w0inv = np.dot(instrument.T, instrument) / nobs
#w0 = np.linalg.inv(w0inv)
mod = gmm.LinearIVGMM(endog, exog, instrument)
res = mod.fit(start,
maxiter=1,
inv_weights=w0inv,
optim_method='bfgs',
optim_args={'gtol': 1e-6},
wargs={'centered': False},
has_optimal_weights=False)
self.res1 = res
mod = gmm.IVGMM(endog, exog, instrument)
res = mod.fit(start,
maxiter=1,
inv_weights=w0inv,
optim_method='bfgs',
optim_args={'gtol': 1e-6},
wargs={'centered': False},
has_optimal_weights=False)
self.res3 = res
from results_gmm_griliches import results_onestep as results
self.res2 = results
