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alkaline-ml / statsmodels python
Repository URL to install this package:
|
Version:
0.11.1 ▾
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'''Results for GMM estimation with Stata gmm
autogenerated, but edited
'''
import numpy as np
from statsmodels.tools.testing import ParamsTableTestBunch
# gmm twostep
est = dict(
rank=13,
N=758,
Q=.0153053843867424,
J=11.60148136515076,
J_df=2,
k_1=13,
converged=1,
has_xtinst=0,
type=1,
n_eq=1,
k=13,
n_moments=15,
k_aux=13,
k_eq_model=0,
k_eq=13,
cmdline="gmm (lw - {xb:s iq expr tenure rns smsa dyear*} - {b0}), instruments(expr tenure rns smsa dyear* med kww age mrt)", # noqa:E501
cmd="gmm",
estat_cmd="gmm_estat",
predict="gmm_p",
marginsnotok="_ALL",
eqnames="1",
technique="gn",
winit="Unadjusted",
estimator="twostep",
wmatrix="robust",
vce="robust",
vcetype="Robust",
params="xb_s xb_iq xb_expr xb_tenure xb_rns xb_smsa xb_dyear_67 xb_dyear_68 xb_dyear_69 xb_dyear_70 xb_dyear_71 xb_dyear_73 b0", # noqa:E501
inst_1="expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 med kww age mrt _cons", # noqa:E501
params_1="xb_s xb_iq xb_expr xb_tenure xb_rns xb_smsa xb_dyear_67 xb_dyear_68 xb_dyear_69 xb_dyear_70 xb_dyear_71 xb_dyear_73 b0", # noqa:E501
sexp_1="lw - ({xb_s} *s + {xb_iq} *iq + {xb_expr} *expr + {xb_tenure} *tenure + {xb_rns} *rns + {xb_smsa} *smsa + {xb_dyear_67} *dyear_67 + {xb_dyear_68} *dyear_68 + {xb_dyear_69} *dyear_69 + {xb_dyear_70} *dyear_70 + {xb_dyear_71} *dyear_71 + {xb_dyear_73} *dyear_73) - {b0}", # noqa:E501
properties="b V",
)
params_table = np.array([
.17579576802018, .02085135579518, 8.4309034744314, 3.430065999e-17,
.1349278616328, .21666367440756, np.nan, 1.9599639845401,
0, -.00928615655842, .00491818692744, -1.8881259893969,
.05900903858228, -.01892562580544, .00035331268861, np.nan,
1.9599639845401, 0, .05028275907275, .00810402250669,
6.2046667603925, 5.481292387e-10, .03439916682973, .06616635131577,
np.nan, 1.9599639845401, 0, .04252138311466,
.00956014552889, 4.4477757149388, 8.676405373e-06, .02378384219108,
.06125892403824, np.nan, 1.9599639845401, 0,
-.10409306762507, .03372997947655, -3.0860696994324, .00202821272955,
-.17020261259839, -.03798352265175, np.nan, 1.9599639845401,
0, .12475123236049, .03098732229429, 4.0258797186699,
.00005676270037, .06401719668634, .18548526803464, np.nan,
1.9599639845401, 0, -.0530431735239, .05178756424595,
-1.0242453819993, .30571938841786, -.15454493429301, .04845858724521,
np.nan, 1.9599639845401, 0, .045954590377,
.0500069437958, .91896418554698, .35811430537838, -.05205721843969,
.14396639919369, np.nan, 1.9599639845401, 0,
.15548006235586, .04801256009054, 3.238320599082, .00120235613037,
.06137717377284, .24958295093889, np.nan, 1.9599639845401,
0, .16698745541498, .06133412154231, 2.7225865670836,
.00647730609718, .04677478616864, .28720012466132, np.nan,
1.9599639845401, 0, .08464846645187, .05581696231774,
1.516536603515, .12938372202927, -.02475076941733, .19404770232108,
np.nan, 1.9599639845401, 0, .0996068440051,
.06123938652803, 1.6265160324477, .10383992558646, -.02042014802516,
.21963383603536, np.nan, 1.9599639845401, 0,
4.0039243732137, .33647541282379, 11.899604608883, 1.189091957e-32,
3.3444446823958, 4.6634040640315, np.nan, 1.9599639845401,
0]).reshape(13, 9)
params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
params_table_rownames = ['_cons'] * 13
cov = np.array([
.0004347790385, -.00007935266682, .00002810133321, .00001482865838,
-.00017806902959, -6.657494262e-06, -.00011587283596, -.00018806600631,
-.00012201035826, -.00008278800394, -.00031494508432, -.00063549805233,
.00264135978484, -.00007935266682, .00002418856265, 4.931157962e-06,
-.00001114070074, .00006618982562, -.0000220304377, 4.705098991e-07,
.00003206383766, -.00002261417242, -.00006024150154, -.00001412826237,
.00001471698858, -.00144299517314, .00002810133321, 4.931157962e-06,
.00006567518079, -.00002036677634, .00005210753053, -.00003295500811,
.00003592276303, .00008754309097, .00003052996731, .00001691178156,
-.00008575591893, -.00013143657018, -.00094318538399, .00001482865838,
-.00001114070074, -.00002036677634, .00009139638253, -.00003771101246,
7.851603106e-06, .00008478298315, .00006722294295, .00011231163007,
.0001007216652, .00011202284802, .00009437878507, .00075643538833,
-.00017806902959, .00006618982562, .00005210753053, -.00003771101246,
.00113771151549, .0001300530702, .00018006693931, .00018772842105,
-9.500874246e-06, .00001633701903, -.00005338908155, .00008260866257,
-.00499436928105, -6.657494262e-06, -.0000220304377, -.00003295500811,
7.851603106e-06, .0001300530702, .00096021414297, .00005702363753,
.00011167528598, .00025281311283, .00010653704891, .00030212216421,
.000307795004, .00157107924026, -.00011587283596, 4.705098991e-07,
.00003592276303, .00008478298315, .00018006693931, .00005702363753,
.00268195181053, .00085892679447, .00091106709634, .00096277498668,
.00090313286214, .00102719488714, .00034624154943, -.00018806600631,
.00003206383766, .00008754309097, .00006722294295, .00018772842105,
.00011167528598, .00085892679447, .0025006944278, .00092531815147,
.00088200162521, .00082339570405, .00095012566921, -.0020631120951,
-.00012201035826, -.00002261417242, .00003052996731, .00011231163007,
-9.500874246e-06, .00025281311283, .00091106709634, .00092531815147,
.00230520592645, .00118209509709, .00111002620771, .00129242901685,
.00256100032752, -.00008278800394, -.00006024150154, .00001691178156,
.0001007216652, .00001633701903, .00010653704891, .00096277498668,
.00088200162521, .00118209509709, .00376187446537, .00124524263644,
.00155856745623, .00599140534858, -.00031494508432, -.00001412826237,
-.00008575591893, .00011202284802, -.00005338908155, .00030212216421,
.00090313286214, .00082339570405, .00111002620771, .00124524263644,
.00311553328238, .00184297728198, .00431291320555, -.00063549805233,
.00001471698858, -.00013143657018, .00009437878507, .00008260866257,
.000307795004, .00102719488714, .00095012566921, .00129242901685,
.00155856745623, .00184297728198, .00375026246233, .00538820067052,
.00264135978484, -.00144299517314, -.00094318538399, .00075643538833,
-.00499436928105, .00157107924026, .00034624154943, -.0020631120951,
.00256100032752, .00599140534858, .00431291320555, .00538820067052,
.11321570343494]).reshape(13, 13)
cov_colnames = ['_cons'] * 13
cov_rownames = ['_cons'] * 13
results_twostep = ParamsTableTestBunch(
params_table=params_table,
params_table_colnames=params_table_colnames,
params_table_rownames=params_table_rownames,
cov=cov,
cov_colnames=cov_colnames,
cov_rownames=cov_rownames,
**est
)
# begin gmm onestep
est = dict(
rank=13,
N=758,
Q=.0175043949471787,
J=13.26833136996146,
J_df=2,
k_1=13,
converged=1,
has_xtinst=0,
type=1,
n_eq=1,
k=13,
n_moments=15,
k_aux=13,
k_eq_model=0,
k_eq=13,
cmdline="gmm (lw - {xb:s iq expr tenure rns smsa dyear*} - {b0}), instruments(expr tenure rns smsa dyear* med kww age mrt) onestep", # noqa:E501
cmd="gmm",
estat_cmd="gmm_estat",
predict="gmm_p",
marginsnotok="_ALL",
eqnames="1",
technique="gn",
winit="Unadjusted",
estimator="onestep",
wmatrix="robust",
vce="robust",
vcetype="Robust",
params="xb_s xb_iq xb_expr xb_tenure xb_rns xb_smsa xb_dyear_67 xb_dyear_68 xb_dyear_69 xb_dyear_70 xb_dyear_71 xb_dyear_73 b0", # noqa:E501
inst_1="expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 med kww age mrt _cons", # noqa:E501
params_1="xb_s xb_iq xb_expr xb_tenure xb_rns xb_smsa xb_dyear_67 xb_dyear_68 xb_dyear_69 xb_dyear_70 xb_dyear_71 xb_dyear_73 b0", # noqa:E501
sexp_1="lw - ({xb_s} *s + {xb_iq} *iq + {xb_expr} *expr + {xb_tenure} *tenure + {xb_rns} *rns + {xb_smsa} *smsa + {xb_dyear_67} *dyear_67 + {xb_dyear_68} *dyear_68 + {xb_dyear_69} *dyear_69 + {xb_dyear_70} *dyear_70 + {xb_dyear_71} *dyear_71 + {xb_dyear_73} *dyear_73) - {b0}", # noqa:E501
properties="b V",
)
params_table = np.array([
.1724253119448, .02073946970972, 8.3138727440057, 9.262847838e-17,
.13177669825528, .21307392563431, np.nan, 1.9599639845401,
0, -.00909883104783, .00488623921543, -1.8621337692816,
.06258423710802, -.01867568392991, .00047802183425, np.nan,
1.9599639845401, 0, .04928948974112, .00804979771953,
6.1230718408647, 9.178828333e-10, .033512176128, .06506680335423,
np.nan, 1.9599639845401, 0, .04221709210829,
.00946363451925, 4.4609808232133, 8.158539147e-06, .0236687092877,
.06076547492887, np.nan, 1.9599639845401, 0,
-.10179345005432, .0337105276595, -3.0196338390938, .00253080446805,
-.16786487016678, -.03572202994187, np.nan, 1.9599639845401,
0, .12611094948071, .0308113805617, 4.0929989887401,
.00004258295784, .06572175326583, .18650014569559, np.nan,
1.9599639845401, 0, -.0596171062088, .05171372339438,
-1.1528295062831, .24898037089783, -.16097414156825, .04173992915064,
np.nan, 1.9599639845401, 0, .04867955998567,
.04981322392381, .97724170714436, .32844950450919, -.04895256485883,
.14631168483017, np.nan, 1.9599639845401, 0,
.15281763323761, .04792849748935, 3.188450321681, .00143037585682,
.05887950432536, .24675576214986, np.nan, 1.9599639845401,
0, .17443605153365, .06112514589814, 2.8537527227227,
.00432061472141, .05463296702353, .29423913604377, np.nan,
1.9599639845401, 0, .0916659665856, .0554618025144,
1.6527765494425, .09837634840443, -.01703716886029, .20036910203149,
np.nan, 1.9599639845401, 0, .09323976498299,
.06084900556261, 1.5323137021041, .125445041459, -.02602209441479,
.21250162438078, np.nan, 1.9599639845401, 0,
4.0335098954259, .33503289261392, 12.03914595954, 2.212341104e-33,
3.3768574922664, 4.6901622985855, np.nan, 1.9599639845401,
0]).reshape(13, 9)
params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
params_table_rownames = ['_cons'] * 13
cov = np.array([
.00043012560384, -.00007821948168, .00002814664044, .00001470659469,
-.00018137297337, -8.404214163e-06, -.000116495836, -.00019098604401,
-.00012670601919, -.00008672920733, -.00031350033095, -.00062509206531,
.00258704275396, -.00007821948168, .00002387533367, 4.911669728e-06,
-.00001098678322, .00006618473561, -.00002158670034, 8.107545213e-07,
.00003255315461, -.00002143051924, -.0000597535309, -.00001402380853,
.00001385883996, -.00142630446035, .00002814664044, 4.911669728e-06,
.00006479924333, -.00001977796199, .00005110284341, -.00003232809926,
.00003557970376, .00008581782553, .00002961847494, .00001478700432,
-.00008727552546, -.00012994173168, -.00094120116335, .00001470659469,
-.00001098678322, -.00001977796199, .00008956037831, -.00003784800308,
7.059546860e-06, .00008151950631, .00006348047144, .00010852497856,
.00009624187488, .00010823787214, .00009132957164, .00074787094553,
-.00018137297337, .00006618473561, .00005110284341, -.00003784800308,
.00113639967508, .00013313518183, .00019039509438, .0002000965573,
7.191780465e-06, .00002329093697, -.00005087978271, .00009086571425,
-.00495748724374, -8.404214163e-06, -.00002158670034, -.00003232809926,
7.059546860e-06, .00013313518183, .00094934117212, .00006195450052,
.00011810217311, .00025505395817, .00011081126685, .00030134673539,
.00030676742472, .00155300401754, -.000116495836, 8.107545213e-07,
.00003557970376, .00008151950631, .00019039509438, .00006195450052,
.00267430918731, .00086135304709, .00092017339035, .00095567351479,
.000887006474, .00102883960359, .0003167617596, -.00019098604401,
.00003255315461, .00008581782553, .00006348047144, .0002000965573,
.00011810217311, .00086135304709, .00248135727768, .0009302682109,
.0008777378644, .00081079994623, .00094288525746, -.00207087031796,
-.00012670601919, -.00002143051924, .00002961847494, .00010852497856,
7.191780465e-06, .00025505395817, .00092017339035, .0009302682109,
.00229714087159, .00117701812554, .00109484405919, .00129252524238,
.00250083573173, -.00008672920733, -.0000597535309, .00001478700432,
.00009624187488, .00002329093697, .00011081126685, .00095567351479,
.0008777378644, .00117701812554, .00373628346107, .00123495172035,
.00154490399953, .00600809353679, -.00031350033095, -.00001402380853,
-.00008727552546, .00010823787214, -.00005087978271, .00030134673539,
.000887006474, .00081079994623, .00109484405919, .00123495172035,
.00307601153815, .0018118788444, .00430884303498, -.00062509206531,
.00001385883996, -.00012994173168, .00009132957164, .00009086571425,
.00030676742472, .00102883960359, .00094288525746, .00129252524238,
.00154490399953, .0018118788444, .00370260147796, .00534911865442,
.00258704275396, -.00142630446035, -.00094120116335, .00074787094553,
-.00495748724374, .00155300401754, .0003167617596, -.00207087031796,
.00250083573173, .00600809353679, .00430884303498, .00534911865442,
.11224703913325]).reshape(13, 13)
cov_colnames = ['_cons'] * 13
cov_rownames = ['_cons'] * 13
results_onestep = ParamsTableTestBunch(
params_table=params_table,
params_table_colnames=params_table_colnames,
params_table_rownames=params_table_rownames,
cov=cov,
cov_colnames=cov_colnames,
cov_rownames=cov_rownames,
**est
)