def setup_class(cls):
# compare to Stata default options, onestep GMM
# this uses maxiter=1, one iteration in loop
cls.params_tol = [1e-5, 1e-6]
cls.bse_tol = [5e-6, 5e-7]
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.IVGMM(endog, exog, instrument)
res = mod.fit(start,
maxiter=1,
inv_weights=w0inv,
optim_method='bfgs',
optim_args={
'gtol': 1e-6,
'disp': 0
},
wargs={'centered': False},
has_optimal_weights=False)
cls.res1 = res
from .results_gmm_griliches import results_onestep as results
cls.res2 = results
def test_ivgmm0_r():
n, k = exog.shape
nobs, k_instr = instrument.shape
w0inv = np.dot(instrument.T, instrument) / nobs
w0 = np.linalg.inv(w0inv)
mod = gmm.IVGMM(endog, exog, instrument)
res = mod.fit(np.ones(exog.shape[1], float),
maxiter=0,
inv_weights=w0inv,
optim_method='bfgs',
optim_args={
'gtol': 1e-8,
'disp': 0
})
assert_allclose(res.params, params, rtol=1e-4, atol=1e-4)
# TODO : res.bse and bse are not the same, rtol=0.09 is large in this case
#res.bse is still robust?, bse is not a sandwich ?
assert_allclose(res.bse, bse, rtol=0.09, atol=0)
score = res.model.score(res.params, w0)
assert_allclose(score, np.zeros(score.shape), rtol=0,
atol=5e-6) # atol=1e-8) ??
def test_ivgmm1_stata():
# copied constant to the beginning
params_stata = np.array([
4.0335099, 0.17242531, -0.00909883, 0.04928949, 0.04221709,
-0.10179345, 0.12611095, -0.05961711, 0.04867956, 0.15281763,
0.17443605, 0.09166597, 0.09323976
])
# robust bse with gmm onestep
bse_stata = np.array([
0.33503289, 0.02073947, 0.00488624, 0.0080498, 0.00946363, 0.03371053,
0.03081138, 0.05171372, 0.04981322, 0.0479285, 0.06112515, 0.0554618,
0.06084901
])
n, k = exog.shape
nobs, k_instr = instrument.shape
w0inv = np.dot(instrument.T, instrument) / nobs
w0 = np.linalg.inv(w0inv)
start = OLS(endog, exog).fit().params
mod = gmm.IVGMM(endog, exog, instrument)
res = mod.fit(start,
maxiter=1,
inv_weights=w0inv,
optim_method='bfgs',
optim_args={
'gtol': 1e-6,
'disp': 0
})
def setup_class(self):
# compare to Stata default options, twostep GMM
self.params_tol = [5e-5, 5e-6]
self.bse_tol = [1e-6, 5e-5]
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.IVGMM(endog, exog, instrument)
res10 = mod.fit(start,
maxiter=2,
inv_weights=w0inv,
optim_method='bfgs',
optim_args={
'gtol': 1e-6,
'disp': 0
},
wargs={'centered': False},
has_optimal_weights=False)
self.res1 = res10
from .results_gmm_griliches import results_twostep as results
self.res2 = results
def setup_class(cls):
# compare to Stata default options, iterative GMM
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.IVGMM(endog, exog, instrument)
res = mod.fit(start,
maxiter=2,
inv_weights=w0inv,
wargs={
'ddof': 0,
'centered': False
},
optim_method='bfgs',
optim_args={
'gtol': 1e-6,
'disp': 0
})
cls.res1 = res
from .results_ivreg2_griliches import results_gmm2s_robust as results
cls.res2 = results
# TODO: remove after testing, compare bse from 1 iteration
# see test_basic
mod = gmm.IVGMM(endog, exog, instrument)
res = mod.fit(start,
maxiter=1,
inv_weights=w0inv,
wargs={
'ddof': 0,
'centered': False
},
optim_method='bfgs',
optim_args={
'gtol': 1e-6,
'disp': 0
})
cls.res3 = res
def setup_class(cls):
exog = exog_st # with const at end
res_ols = OLS(endog, exog).fit()
# use exog as instrument
nobs, k_instr = exog.shape
w0inv = np.dot(exog.T, exog) / nobs
#w0 = np.linalg.inv(w0inv)
mod = gmm.IVGMM(endog, exog, exog)
res = mod.fit(np.ones(exog.shape[1], float), maxiter=0, inv_weights=w0inv,
optim_method='bfgs', optim_args={'gtol':1e-6, 'disp': 0})
cls.res1 = res
cls.res2 = res_ols
def setup_class(cls):
#cls.bse_tol = [5e-7, 5e-7]
# compare to Stata default options, iterative GMM
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.IVGMM(endog, exog, instrument)
res10 = mod.fit(start, maxiter=10, inv_weights=w0inv,
optim_method='bfgs', optim_args={'gtol':1e-6, 'disp': 0},
wargs={'centered':False})
cls.res1 = res10
from .results_gmm_griliches_iter import results
cls.res2 = results
def setup_class(cls):
# compare to Stata default options, onestep GMM
cls.params_tol = [5e-4, 5e-5]
cls.bse_tol = [7e-3, 5e-4]
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.IVGMM(endog, exog, instrument)
res = mod.fit(start, maxiter=0, inv_weights=w0inv,
optim_method='bfgs',
optim_args={'gtol':1e-6, 'disp': 0})
cls.res1 = res
from .results_gmm_griliches import results_onestep as results
cls.res2 = results
def setup_class(self):
# compare to Stata default options, onestep GMM
# this uses maxiter=1, one iteration in loop
self.params_tol = [5e-4, 5e-5]
self.bse_tol = [7e-3, 5e-4]
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.IVGMM(endog, exog, instrument)
res = mod.fit(start,
maxiter=1,
inv_weights=w0inv,
optim_method='bfgs',
optim_args={'gtol': 1e-6})
self.res1 = res
from results_gmm_griliches import results_onestep as results
self.res2 = results
def setup_class(self):
# compare to Stata default options, onestep GMM
# this uses maxiter=1, one iteration in loop
self.params_tol = [5e-5, 5e-6]
self.bse_tol = [5e-6, 1e-1]
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)
def func(params, exog):
return np.dot(exog, params)
mod = gmm.NonlinearIVGMM(endog, exog, instrument, func)
res = mod.fit(start,
maxiter=1,
inv_weights=w0inv,
optim_method='bfgs',
optim_args={'gtol': 1e-8},
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
return endog, exog, instrument
if exampledata == 'ols':
endog, exog, _ = sample_ols(X)
instrument = exog
elif exampledata == 'iv':
endog, exog, instrument = sample_iv(X)
elif exampledata == 'ivfake':
endog, exog, instrument = sample_ivfake(X)
#using GMM and IV2SLS classes
#----------------------------
mod = gmm.IVGMM(endog, exog, instrument, nmoms=instrument.shape[1])
res = mod.fit()
modgmmols = gmm.IVGMM(endog, exog, exog, nmoms=exog.shape[1])
resgmmols = modgmmols.fit()
#the next is the same as IV2SLS, (Z'Z)^{-1} as weighting matrix
modgmmiv = gmm.IVGMM(endog, exog, instrument, nmoms=instrument.shape[1]) #same as mod
resgmmiv = modgmmiv.fitgmm(np.ones(exog.shape[1], float),
weights=np.linalg.inv(np.dot(instrument.T, instrument)))
modls = gmm.IV2SLS(endog, exog, instrument)
resls = modls.fit()
modols = OLS(endog, exog)
resols = modols.fit()
print('\nIV case')
print('params')
print('IV2SLS', resls.params)
