def test_gmm_cue_optimization_options(data):
mod = IVGMMCUE(data.dep, data.exog, data.endog, data.instr)
res_none = mod.fit(display=False)
opt_options = dict(method="BFGS", options={"disp": False})
res_bfgs = mod.fit(display=False, opt_options=opt_options)
opt_options = dict(method="L-BFGS-B", options={"disp": False})
res_lbfgsb = mod.fit(display=False, opt_options=opt_options)
assert res_none.iterations > 2
assert res_bfgs.iterations > 2
assert res_lbfgsb.iterations >= 1
mod2 = IVGMM(data.dep, data.exog, data.endog, data.instr)
res2 = mod2.fit()
assert res_none.j_stat.stat <= res2.j_stat.stat
assert res_bfgs.j_stat.stat <= res2.j_stat.stat
assert res_lbfgsb.j_stat.stat <= res2.j_stat.stat
def test_gmm_cue_starting_vals(data):
mod = IVGMM(data.dep, data.exog, data.endog, data.instr)
sv = mod.fit().params
mod = IVGMMCUE(data.dep, data.exog, data.endog, data.instr)
mod.fit(starting=sv, display=False)
with pytest.raises(ValueError):
mod.fit(starting=sv[:-1], display=True)
def test_gmm_cue(data):
mod = IVGMMCUE(data.dep, data.exog, data.endog, data.instr)
res = mod.fit(display=False)
assert res.iterations > 2
mod2 = IVGMM(data.dep, data.exog, data.endog, data.instr)
res2 = mod2.fit()
assert res.j_stat.stat <= res2.j_stat.stat
mod = IVGMMCUE(data.dep, data.exog, data.endog, data.instr, center=False)
res = mod.fit(display=False)
mod2 = IVGMM(data.dep, data.exog, data.endog, data.instr, center=False)
res2 = mod2.fit()
assert res.j_stat.stat <= res2.j_stat.stat