def test_formula_equivalence_weights(data):
weights = AttrDict()
eqn_copy = AttrDict()
for key in data.eqns:
eqn = {k: v for k, v in data.eqns[key].items()}
nobs = eqn["dependent"].shape[0]
w = np.random.chisquare(2, (nobs, 1)) / 2
weights[key] = w
eqn["weights"] = w
eqn_copy[key] = eqn
mod = IVSystemGMM(eqn_copy, weight_type="unadjusted")
df = []
formulas = {}
for i, key in enumerate(data.eqns):
eqn = data.eqns[key]
dep = eqn.dependent
ex = eqn.exog
en = eqn.endog
instr = eqn.instruments
dep = DataFrame(dep, columns=["dep_{0}".format(i)])
has_const = False
if np.any(np.all(ex == 1, 0)):
ex = ex[:, 1:]
has_const = True
ex = DataFrame(
ex,
columns=["ex_{0}_{1}".format(i, j) for j in range(ex.shape[1])])
en = DataFrame(
en,
columns=["en_{0}_{1}".format(i, j) for j in range(en.shape[1])])
instr = DataFrame(
instr,
columns=["instr_{0}_{1}".format(i, j) for j in range(ex.shape[1])])
fmla = "".join(dep.columns) + " ~ "
if has_const:
fmla += " 1 + "
fmla += " + ".join(ex.columns) + " + ["
fmla += " + ".join(en.columns) + " ~ "
fmla += " + ".join(instr.columns) + " ] "
formulas[key] = fmla
df.extend([dep, ex, en, instr])
df = concat(df, 1)
formula_mod = IVSystemGMM.from_formula(formulas,
df,
weights=weights,
weight_type="unadjusted")
res = mod.fit(cov_type="unadjusted")
formula_res = formula_mod.fit(cov_type="unadjusted")
assert_allclose(res.params, formula_res.params)
def test_formula_equivalence(data):
mod = IVSystemGMM(data.eqns, weight_type="unadjusted")
formula = []
df = []
for i, key in enumerate(data.eqns):
eqn = data.eqns[key]
dep = eqn.dependent
ex = eqn.exog
en = eqn.endog
instr = eqn.instruments
dep = DataFrame(dep, columns=["dep_{0}".format(i)])
has_const = False
if np.any(np.all(ex == 1, 0)):
ex = ex[:, 1:]
has_const = True
ex = DataFrame(
ex,
columns=["ex_{0}_{1}".format(i, j) for j in range(ex.shape[1])])
en = DataFrame(
en,
columns=["en_{0}_{1}".format(i, j) for j in range(en.shape[1])])
instr = DataFrame(
instr,
columns=["instr_{0}_{1}".format(i, j) for j in range(ex.shape[1])])
fmla = "".join(dep.columns) + " ~ "
if has_const:
fmla += " 1 + "
fmla += " + ".join(ex.columns) + " + ["
fmla += " + ".join(en.columns) + " ~ "
fmla += " + ".join(instr.columns) + " ] "
formula.append(fmla)
df.extend([dep, ex, en, instr])
formulas = {}
for i, f in enumerate(formula):
formulas["eq{0}".format(i)] = f
df = concat(df, 1)
formula_mod = IVSystemGMM.from_formula(formulas,
df,
weight_type="unadjusted")
res = mod.fit(cov_type="unadjusted")
formula_res = formula_mod.fit(cov_type="unadjusted")
assert_allclose(res.params, formula_res.params)
def test_formula_equivalence_weights(data):
weights = AttrDict()
eqn_copy = AttrDict()
for key in data.eqns:
eqn = {k: v for k, v in data.eqns[key].items()}
nobs = eqn['dependent'].shape[0]
w = np.random.chisquare(2, (nobs, 1)) / 2
weights[key] = w
eqn['weights'] = w
eqn_copy[key] = eqn
mod = IVSystemGMM(eqn_copy, weight_type='unadjusted')
df = []
formulas = OrderedDict()
for i, key in enumerate(data.eqns):
eqn = data.eqns[key]
dep = eqn.dependent
ex = eqn.exog
en = eqn.endog
instr = eqn.instruments
dep = pd.DataFrame(dep, columns=['dep_{0}'.format(i)])
has_const = False
if np.any(np.all(ex == 1, 0)):
ex = ex[:, 1:]
has_const = True
ex = pd.DataFrame(ex, columns=['ex_{0}_{1}'.format(i, j) for j in range(ex.shape[1])])
en = pd.DataFrame(en, columns=['en_{0}_{1}'.format(i, j) for j in range(en.shape[1])])
instr = pd.DataFrame(instr, columns=['instr_{0}_{1}'.format(i, j)
for j in range(ex.shape[1])])
fmla = ''.join(dep.columns) + ' ~ '
if has_const:
fmla += ' 1 + '
fmla += ' + '.join(ex.columns) + ' + ['
fmla += ' + '.join(en.columns) + ' ~ '
fmla += ' + '.join(instr.columns) + ' ] '
formulas[key] = fmla
df.extend([dep, ex, en, instr])
df = pd.concat(df, 1)
formula_mod = IVSystemGMM.from_formula(formulas, df, weights=weights, weight_type='unadjusted')
res = mod.fit(cov_type='unadjusted')
formula_res = formula_mod.fit(cov_type='unadjusted')
assert_allclose(res.params, formula_res.params)
def test_formula_equivalence(data):
mod = IVSystemGMM(data.eqns, weight_type='unadjusted')
formula = []
df = []
for i, key in enumerate(data.eqns):
eqn = data.eqns[key]
dep = eqn.dependent
ex = eqn.exog
en = eqn.endog
instr = eqn.instruments
dep = pd.DataFrame(dep, columns=['dep_{0}'.format(i)])
has_const = False
if np.any(np.all(ex == 1, 0)):
ex = ex[:, 1:]
has_const = True
ex = pd.DataFrame(ex, columns=['ex_{0}_{1}'.format(i, j) for j in range(ex.shape[1])])
en = pd.DataFrame(en, columns=['en_{0}_{1}'.format(i, j) for j in range(en.shape[1])])
instr = pd.DataFrame(instr, columns=['instr_{0}_{1}'.format(i, j)
for j in range(ex.shape[1])])
fmla = ''.join(dep.columns) + ' ~ '
if has_const:
fmla += ' 1 + '
fmla += ' + '.join(ex.columns) + ' + ['
fmla += ' + '.join(en.columns) + ' ~ '
fmla += ' + '.join(instr.columns) + ' ] '
formula.append(fmla)
df.extend([dep, ex, en, instr])
from collections import OrderedDict
formulas = OrderedDict()
for i, f in enumerate(formula):
formulas['eq{0}'.format(i)] = f
df = pd.concat(df, 1)
formula_mod = IVSystemGMM.from_formula(formulas, df, weight_type='unadjusted')
res = mod.fit(cov_type='unadjusted')
formula_res = formula_mod.fit(cov_type='unadjusted')
assert_allclose(res.params, formula_res.params)