def fit(request):
method = request.param
data = generate_simultaneous_data()
if 'ols' in method or 'sur' in method:
mod = SUR
for key in data:
temp = data[key]
temp['exog'] = concat([temp['exog'], temp['endog']], 1)
del temp['endog']
del temp['instruments']
else:
mod = IV3SLS
if 'ols' in method or '2sls' in method:
fit_method = 'ols'
else:
fit_method = 'gls'
mod = mod(data)
iterate = 'ireg3' in method
stata = results[method]
debiased = method in ('ols', '2sls')
kwargs = {}
decimal = 2 if 'ireg3' in method else 5
rtol = 10**-decimal
res = mod.fit(cov_type='unadjusted',
method=fit_method,
debiased=debiased,
iterate=iterate,
**kwargs)
return stata, res, rtol
def fit(request):
method = request.param
data = generate_simultaneous_data()
if "ols" in method or "sur" in method:
mod = SUR
for key in data:
temp = data[key]
temp["exog"] = concat([temp["exog"], temp["endog"]], axis=1, sort=False)
del temp["endog"]
del temp["instruments"]
else:
mod = IV3SLS
if "ols" in method or "2sls" in method:
fit_method = "ols"
else:
fit_method = "gls"
mod = mod(data)
iterate = "ireg3" in method
stata = results[method]
debiased = method in ("ols", "2sls")
kwargs = {}
decimal = 3 if "ireg3" in method else 5
rtol = 10**-decimal
res = mod.fit(
cov_type="unadjusted",
method=fit_method,
debiased=debiased,
iterate=iterate,
**kwargs,
)
return stata, res, rtol
"""
Important cases
"""
import os
import subprocess
from typing import List
import pandas as pd
from linearmodels.tests.system._utility import generate_simultaneous_data
data = generate_simultaneous_data()
all_cols: List[str] = []
out = []
for key in data:
eqn = data[key]
for key in ("exog", "endog"):
vals = eqn[key]
for col in vals:
if col in all_cols:
continue
else:
out.append(vals[col])
all_cols.append(col)
out_df = pd.concat(out, 1, sort=False)
if "const" in out_df:
out_df.pop("const")
out_df.to_stata("simulated-3sls.dta", write_index=False)
SEP = """