def test_no_exog():
data = generate_3sls_data_v2(nexog=0, const=False)
mod = IV3SLS(data)
res = mod.fit()
data = generate_3sls_data_v2(nexog=0, const=False, omitted="drop")
mod = IV3SLS(data)
res2 = mod.fit()
data = generate_3sls_data_v2(nexog=0, const=False, omitted="empty")
mod = IV3SLS(data)
res3 = mod.fit()
data = generate_3sls_data_v2(nexog=0, const=False, output_dict=False)
mod = IV3SLS(data)
res4 = mod.fit()
data = generate_3sls_data_v2(
nexog=0, const=False, output_dict=False, omitted="empty"
)
mod = IV3SLS(data)
res5 = mod.fit()
assert_series_equal(res.params, res2.params)
assert_series_equal(res.params, res3.params)
assert_series_equal(res.params, res4.params)
assert_series_equal(res.params, res5.params)
def test_no_endog():
data = generate_3sls_data_v2(nendog=0, ninstr=0)
mod = IV3SLS(data)
res = mod.fit()
data = generate_3sls_data_v2(nendog=0, ninstr=0, omitted='drop')
mod = IV3SLS(data)
res2 = mod.fit()
data = generate_3sls_data_v2(nendog=0, ninstr=0, omitted='empty')
mod = IV3SLS(data)
res3 = mod.fit()
data = generate_3sls_data_v2(nendog=0, ninstr=0, output_dict=False)
mod = IV3SLS(data)
res4 = mod.fit()
data = generate_3sls_data_v2(nendog=0,
ninstr=0,
output_dict=False,
omitted='empty')
mod = IV3SLS(data)
res5 = mod.fit()
assert_series_equal(res.params, res2.params)
assert_series_equal(res.params, res3.params)
assert_series_equal(res.params, res4.params)
assert_series_equal(res.params, res5.params)
def cov_data():
data = generate_3sls_data_v2(k=2)
mod = IV3SLS(data)
res = mod.fit(cov_type="unadjusted")
x = mod._x
z = mod._z
eps = res.resids.values
sigma = res.sigma
return x, z, eps, sigma
def cov(request):
eqns = generate_3sls_data_v2(k=3)
est = request.param[0]
name = request.param[1]
sigma = full_sigma = np.eye(3)
x = [eqns[key].exog for key in eqns]
n = x[0].shape[0]
eps = np.random.standard_normal((n, 3))
return est(x, eps, sigma, full_sigma, gls=True, debiased=True), name
def weight_data():
eqns = generate_3sls_data_v2(k=2)
mod = IV3SLS(eqns)
x = mod._x
z = mod._z
res = mod.fit(cov_type="unadjusted")
eps = np.asarray(res.resids)
sigma = np.asarray(res.sigma)
return x, z, eps, sigma
def cov_data():
data = generate_3sls_data_v2(k=2)
mod = IV3SLS(data)
res = mod.fit(cov_type="unadjusted")
x = mod._x
z = mod._z
eps = np.asarray(res.resids)
sigma = np.asarray(res.sigma)
return x, z, eps, sigma
def weight_data():
eqns = generate_3sls_data_v2(k=2)
mod = IV3SLS(eqns)
x = mod._x
z = mod._z
res = mod.fit(cov_type="unadjusted")
eps = res.resids.values
sigma = res.sigma
return x, z, eps, sigma
def data(request):
steps, robust = request.param
weight_type = 'robust' if robust else 'unadjusted'
eqns = generate_3sls_data_v2(k=3)
y = [eqns[key].dependent for key in eqns]
x = [np.concatenate([eqns[key].exog, eqns[key].endog], 1) for key in eqns]
z = [np.concatenate([eqns[key].exog, eqns[key].instruments], 1) for key in eqns]
return AttrDict(eqns=eqns, x=x, y=y, z=z, steps=steps,
robust=robust, weight_type=weight_type)
def test_gmm_equiv():
eqns = generate_3sls_data_v2(k=1)
sys_mod = IVSystemGMM(eqns)
eqn = eqns[list(eqns.keys())[0]]
gmm_mod = IVGMM(eqn.dependent, eqn.exog, eqn.endog, eqn.instruments)
sys_res = sys_mod.fit()
gmm_res = gmm_mod.fit()
assert_allclose(sys_res.params, gmm_res.params)
assert_allclose(sys_res.rsquared, gmm_res.rsquared)
assert_allclose(sys_res.tstats, gmm_res.tstats)
def gmm_cov(request):
eqns = generate_3sls_data_v2(k=3)
est = request.param[0]
name = request.param[1]
sigma = np.eye(3)
x = [eqns[key].exog for key in eqns]
z = [np.concatenate([eqns[key].exog, eqns[key].instruments], 1) for key in eqns]
kz = sum(map(lambda a: a.shape[1], z))
w = np.eye(kz)
n = x[0].shape[0]
eps = np.random.standard_normal((n, 3))
return est(x, z, eps, w, sigma=sigma), name
def test_3sls_2sls_equiv():
eqns = generate_3sls_data_v2(k=1)
tsls_mod = IV3SLS(eqns)
tsls = tsls_mod.fit(method='ols', cov_type='unadjusted', debiased=False)
eqn = eqns[list(eqns.keys())[0]]
ivmod = IV2SLS(eqn.dependent, eqn.exog, eqn.endog, eqn.instruments)
iv = ivmod.fit(cov_type='unadjusted', debiased=False)
assert_allclose(iv.params, tsls.params)
assert_allclose(iv.tstats, tsls.tstats)
assert_allclose(iv.rsquared, tsls.rsquared)
tsls = tsls_mod.fit(method='ols', cov_type='unadjusted', debiased=True)
iv = ivmod.fit(cov_type='unadjusted', debiased=True)
assert_allclose(iv.tstats, tsls.tstats)
tsls = tsls_mod.fit(method='ols', cov_type='robust', debiased=False)
iv = ivmod.fit(cov_type='robust', debiased=False)
assert_allclose(iv.tstats, tsls.tstats)
def test_gmm_3sls_equiv():
eqns = generate_3sls_data_v2(k=3)
gmm = IVSystemGMM(eqns).fit(iter_limit=1)
tsls = IV3SLS(eqns).fit(method='ols')
assert_allclose(gmm.params, tsls.params)
def test_no_constant_smoke():
eqns = generate_3sls_data_v2(k=3, const=False)
mod = IVSystemGMM(eqns)
mod.fit()
def test_uneven_shapes():
data = generate_3sls_data_v2()
eq = data[list(data.keys())[0]]
eq['weights'] = np.ones(eq.dependent.shape[0] // 2)
with pytest.raises(ValueError):
IV3SLS(data)
from collections import OrderedDict
from itertools import product
import numpy as np
import pytest
from pandas import Series, concat
from pandas.testing import assert_series_equal, assert_frame_equal
from linearmodels import SUR, IVSystemGMM, IV3SLS
from linearmodels.formula import sur, iv_system_gmm, iv_3sls
from linearmodels.system.model import SystemFormulaParser
from linearmodels.tests.system._utility import generate_3sls_data_v2
from linearmodels.utility import AttrDict
data = generate_3sls_data_v2(k=2, const=False)
joined = []
for i, key in enumerate(data):
eq = data[key]
joined.append(Series(eq.dependent[:, 0], name='y{0}'.format(i + 1)))
for j, col in enumerate(eq.exog.T):
joined.append(Series(col, name='x{0}{1}'.format(i + 1, j + 1)))
k = len(eq.exog.T)
for j, col in enumerate(eq.endog.T):
joined.append(Series(col, name='x{0}{1}'.format(i + 1, j + k + 1)))
for j, col in enumerate(eq.instruments.T):
joined.append(Series(col, name='z{0}{1}'.format(i + 1, j + 1)))
joined = concat(joined, 1)
fmlas = [{
'eq1': 'y1 ~ x11 + x12',
'eq2': 'y2 ~ x21 + x22'
