import numpy as np from statsmodels.tools.testing import ParamsTableTestBunch param_names = [ 's', 'iq', 'expr', 'tenure', 'rns', 'smsa', 'dyear_67', 'dyear_68', 'dyear_69', 'dyear_70', 'dyear_71', 'dyear_73', '_cons'] est = dict( N=758, inexog_ct=10, exexog_ct=4, endog_ct=2, partial_ct=0, df_m=12, sdofminus=0, dofminus=0, r2=.2279825291623523, rmse=.3766456260250817, rss=107.5313411236999, mss=31.75480871825532, r2_a=.2155473484240278, F=37.63903370585436, Fp=1.04881083780e-68, Fdf1=12, Fdf2=745, yy=24652.2466174172, yyc=139.2861498419552, r2u=.9956380713371686, partialcons=0, cons=1, cdf=12.55161416131593, widstat=12.55161416131593, cd=.0675726199801665, idp=2.15251425210e-10, iddf=3, idstat=47.97804382236573, sarganp=.0013146751383334, sargandf=2, sargan=13.26833137393004, jp=.0013146751383334, jdf=2, j=13.26833137393004, ll=-335.4059158173529, rankV=13, rankxx=13, rankzz=15, r2c=.2279825291623523, hacsubtitleV="Statistics consistent for homoskedasticity only", hacsubtitleB="Estimates efficient for homoskedasticity only", title="IV (2SLS) estimation", predict="ivreg2_p", version="02.2.08", cmdline="ivreg2 lw expr tenure rns smsa dyear* (s iq=med kww age mrt)", cmd="ivreg2", model="iv", depvar="lw", partialsmall="small", exexog="med kww age mrt", inexog="expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73", # noqa:E501 insts="med kww age mrt expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73", # noqa:E501 instd="s iq", properties="b V", ) params_table = np.array([ .17242531190423, .02073807855911, 8.3144304527925, 9.219394861e-17, .13177942481982, .21307119898864, np.nan, 1.9599639845401, 0, -.00909883103476, .00470440157185, -1.9341101935687, .05309958088226, -.01831928868441, .00012162661488, np.nan, 1.9599639845401, 0, .04928948974574, .008154589406, 6.0443864542646, 1.499796319e-09, .03330678820126, .06527219129022, np.nan, 1.9599639845401, 0, .04221709210309, .00884287446052, 4.774136768712, 1.804797032e-06, .02488537664065, .05954880756552, np.nan, 1.9599639845401, 0, -.10179345001799, .03417647128271, -2.9784657747708, .00289695381119, -.16877810285078, -.03480879718521, np.nan, 1.9599639845401, 0, .12611094946923, .03092747922238, 4.0776342799379, .00004549625503, .06549420406076, .18672769487769, np.nan, 1.9599639845401, 0, -.05961710621535, .05529546018534, -1.0781555305902, .28096435347028, -.16799421668718, .04876000425648, np.nan, 1.9599639845401, 0, .04867955999401, .05201609347935, .93585574651683, .34934746462842, -.05327010984199, .15062922983, np.nan, 1.9599639845401, 0, .15281763322545, .0515629903814, 2.9637077309732, .0030395682829, .05175602914272, .25387923730817, np.nan, 1.9599639845401, 0, .17443605148569, .05975759031645, 2.9190610023255, .00351087511908, .05731332666255, .29155877630882, np.nan, 1.9599639845401, 0, .09166596656323, .05414400395495, 1.6930031003894, .09045487706511, -.01445433116727, .19778626429372, np.nan, 1.9599639845401, 0, .09323976497853, .0571819085978, 1.6305815469428, .10297864547348, -.01883471644041, .20531424639747, np.nan, 1.9599639845401, 0, 4.0335098946211, .31542152364325, 12.787681221092, 1.921209213e-37, 3.4152950683316, 4.6517247209107, np.nan, 1.9599639845401, 0]).reshape(13, 9) params_table_colnames = 'b se z pvalue ll ul df crit eform'.split() params_table_rownames = param_names cov = np.array([ .00043006790232, -.00007669911732, .00003390383154, .00001947510761, -.00014211117725, .00001984430949, .00001313809086, -.00010726217801, -.0000934373525, .00003291577926, -.00026067342405, -.00058025280789, .00230075400852, -.00007669911732, .00002213139415, 4.388754481e-06, -8.774272042e-06, .00005855699916, -.00001627155824, -.00001175114527, .00001447038262, -.00001630144788, -.00007254738147, -.0000221423658, .00001715527313, -.00125964216852, .00003390383154, 4.388754481e-06, .00006649732838, -.00001561989737, .00003340985346, -6.286271714e-06, .00004284715305, .00006535720207, .00002474647562, 2.348670591e-06, -.00012774309302, -.00013889592824, -.00097053265371, .00001947510761, -8.774272042e-06, -.00001561989737, .00007819642872, -.00001656102532, -1.454183906e-06, .00006415542548, .0000658729123, .00007759572764, .00006755177717, .00006035533972, .00007925563165, .00048915076309, -.00014211117725, .00005855699916, .00003340985346, -.00001656102532, .00116803118934, .00009910080232, .00010687488928, .0000916567869, -.00004794231232, -.00018139826234, -.00013904727086, -.00016941373862, -.00453388036786, .00001984430949, -.00001627155824, -6.286271714e-06, -1.454183906e-06, .00009910080232, .00095650897105, 2.293607157e-06, -4.175180521e-06, .00008023363682, .0000277191399, .00011455631826, 7.627834100e-06, .00071035658346, .00001313809086, -.00001175114527, .00004284715305, .00006415542548, .00010687488928, 2.293607157e-06, .00305758791711, .00082806878751, .00084056268139, .00086879959596, .00072687243235, .00079702544855, .00005969922435, -.00010726217801, .00001447038262, .00006535720207, .0000658729123, .0000916567869, -4.175180521e-06, .00082806878751, .00270567398085, .00090000327564, .0008652672979, .00075215815711, .00093055832029, -.0011308128858, -.0000934373525, -.00001630144788, .00002474647562, .00007759572764, -.00004794231232, .00008023363682, .00084056268139, .00090000327564, .00265874197707, .00106768253686, .00097403133741, .00118960267354, .00179874271988, .00003291577926, -.00007254738147, 2.348670591e-06, .00006755177717, -.00018139826234, .0000277191399, .00086879959596, .0008652672979, .00106768253686, .00357096960043, .00115404290469, .00134956217212, .00598978065251, -.00026067342405, -.0000221423658, -.00012774309302, .00006035533972, -.00013904727086, .00011455631826, .00072687243235, .00075215815711, .00097403133741, .00115404290469, .00293157316427, .00171538928992, .0048030008792, -.00058025280789, .00001715527313, -.00013889592824, .00007925563165, -.00016941373862, 7.627834100e-06, .00079702544855, .00093055832029, .00118960267354, .00134956217212, .00171538928992, .00326977067089, .00483241607215, .00230075400852, -.00125964216852, -.00097053265371, .00048915076309, -.00453388036786, .00071035658346, .00005969922435, -.0011308128858, .00179874271988, .00598978065251, .0048030008792, .00483241607215, .09949073757743]).reshape(13, 13) cov_colnames = param_names cov_rownames = param_names results = 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 ) est = dict( N=758, inexog_ct=10, exexog_ct=4, endog_ct=2, partial_ct=0, df_m=12, sdofminus=0, dofminus=0, r2=.2279825291623523, rmse=.3766456260250817, rss=107.5313411236999, mss=31.75480871825532, r2_a=.2155473484240278, F=40.08955571761713, Fp=1.50331141073e-72, Fdf1=12, Fdf2=745, yy=24652.2466174172, yyc=139.2861498419552, r2u=.9956380713371686, partialcons=0, cons=1, cdf=12.55161416131593, widstat=11.46142788662503, cd=.0675726199801665, idp=6.77658650925e-09, iddf=3, idstat=40.92698219921901, jp=.0030253131145893, jdf=2, j=11.60148136780177, ll=-335.4059158173529, rankV=13, rankS=15, rankxx=13, rankzz=15, r2c=.2279825291623523, hacsubtitleV="Statistics robust to heteroskedasticity", hacsubtitleB="Estimates efficient for homoskedasticity only", title="IV (2SLS) estimation", predict="ivreg2_p", version="02.2.08", cmdline="ivreg2 lw expr tenure rns smsa dyear* (s iq=med kww age mrt), robust", # noqa:E501 cmd="ivreg2", model="iv", depvar="lw", vcetype="Robust", partialsmall="small", exexog="med kww age mrt", inexog="expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73", # noqa:E501 insts="med kww age mrt expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73", # noqa:E501 instd="s iq", properties="b V", ) params_table = np.array([ .17242531190423, .02073946970741, 8.3138727429773, 9.262847918e-17, .13177669821925, .21307392558922, np.nan, 1.9599639845401, 0, -.00909883103476, .00488623921475, -1.862133766863, .06258423744885, -.01867568391553, .000478021846, np.nan, 1.9599639845401, 0, .04928948974574, .00804979771814, 6.1230718424932, 9.178828239e-10, .03351217613534, .06506680335614, np.nan, 1.9599639845401, 0, .04221709210309, .00946363451747, 4.4609808235038, 8.158539136e-06, .02366870928599, .06076547492018, np.nan, 1.9599639845401, 0, -.10179345001799, .03371052765435, -3.0196338384772, .0025308044732, -.16786487012036, -.03572202991563, np.nan, 1.9599639845401, 0, .12611094946923, .03081138055695, 4.0929989889975, .0000425829578, .06572175326364, .18650014567481, np.nan, 1.9599639845401, 0, -.05961710621535, .05171372338658, -1.1528295065836, .24898037077447, -.16097414155951, .04173992912881, np.nan, 1.9599639845401, 0, .04867955999401, .04981322391886, .97724170740882, .32844950437829, -.04895256484079, .14631168482881, np.nan, 1.9599639845401, 0, .15281763322545, .04792849748217, 3.1884503219051, .00143037585571, .05887950432728, .24675576212361, np.nan, 1.9599639845401, 0, .17443605148569, .06112514588945, 2.8537527223437, .00432061472656, .0546329669926, .29423913597877, np.nan, 1.9599639845401, 0, .09166596656323, .05546180250571, 1.6527765492979, .09837634843385, -.01703716886563, .20036910199209, np.nan, 1.9599639845401, 0, .09323976497853, .06084900555321, 1.5323137022675, .12544504141872, -.02602209440084, .21250162435791, np.nan, 1.9599639845401, 0, 4.0335098946211, .33503289255951, 12.039145959093, 2.212341116e-33, 3.3768574915682, 4.6901622976741, np.nan, 1.9599639845401, 0]).reshape(13, 9) params_table_colnames = 'b se z pvalue ll ul df crit eform'.split() params_table_rownames = param_names cov = np.array([ .00043012560374, -.00007821948167, .00002814664043, .00001470659468, -.0001813729733, -8.404214148e-06, -.00011649583596, -.000190986044, -.0001267060192, -.00008672920729, -.00031350033086, -.00062509206513, .00258704275336, -.00007821948167, .00002387533366, 4.911669726e-06, -.00001098678321, .00006618473559, -.00002158670033, 8.107545182e-07, .00003255315461, -.00002143051923, -.00005975353088, -.00001402380853, .00001385883995, -.00142630445991, .00002814664043, 4.911669726e-06, .0000647992433, -.00001977796198, .00005110284339, -.00003232809925, .00003557970375, .00008581782551, .00002961847493, .00001478700432, -.00008727552542, -.00012994173162, -.000941201163, .00001470659468, -.00001098678321, -.00001977796198, .00008956037828, -.00003784800305, 7.059546852e-06, .0000815195063, .00006348047141, .00010852497853, .00009624187487, .0001082378721, .0000913295716, .00074787094515, -.0001813729733, .00006618473559, .00005110284339, -.00003784800305, .00113639967473, .0001331351818, .00019039509428, .00020009655722, 7.191780470e-06, .00002329093699, -.00005087978262, .00009086571417, -.0049574872418, -8.404214148e-06, -.00002158670033, -.00003232809925, 7.059546852e-06, .0001331351818, .00094934117183, .00006195450043, .00011810217306, .00025505395801, .00011081126682, .0003013467353, .00030676742453, .00155300401661, -.00011649583596, 8.107545182e-07, .00003557970375, .0000815195063, .00019039509428, .00006195450043, .0026743091865, .00086135304687, .00092017339013, .00095567351458, .00088700647379, .00102883960334, .00031676175972, -.000190986044, .00003255315461, .00008581782551, .00006348047141, .00020009655722, .00011810217306, .00086135304687, .00248135727719, .00093026821071, .00087773786421, .00081079994607, .0009428852573, -.00207087031744, -.0001267060192, -.00002143051923, .00002961847493, .00010852497853, 7.191780470e-06, .00025505395801, .00092017339013, .00093026821071, .0022971408709, .00117701812528, .00109484405896, .00129252524214, .00250083573092, -.00008672920729, -.00005975353088, .00001478700432, .00009624187487, .00002329093699, .00011081126682, .00095567351458, .00087773786421, .00117701812528, .00373628346001, .00123495172003, .00154490399913, .00600809353497, -.00031350033086, -.00001402380853, -.00008727552542, .0001082378721, -.00005087978262, .0003013467353, .00088700647379, .00081079994607, .00109484405896, .00123495172003, .00307601153718, .00181187884386, .00430884303329, -.00062509206513, .00001385883995, -.00012994173162, .0000913295716, .00009086571417, .00030676742453, .00102883960334, .0009428852573, .00129252524214, .00154490399913, .00181187884386, .00370260147681, .00534911865268, .00258704275336, -.00142630445991, -.000941201163, .00074787094515, -.0049574872418, .00155300401661, .00031676175972, -.00207087031744, .00250083573092, .00600809353497, .00430884303329, .00534911865268, .11224703909679]).reshape(13, 13) cov_colnames = param_names cov_rownames = param_names results_robust = 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 ) est = dict( N=758, inexog_ct=10, exexog_ct=4, endog_ct=2, partial_ct=0, df_r=745, df_m=12, sdofminus=0, dofminus=0, r2=.2279825291623523, rmse=.3799175840045295, rss=107.5313411236999, mss=31.75480871825532, r2_a=.2155473484240278, F=37.63903370585438, Fp=1.04881083780e-68, Fdf1=12, Fdf2=745, yy=24652.2466174172, yyc=139.2861498419552, partialcons=0, cons=1, cdf=12.55161416131593, widstat=12.55161416131593, cd=.0675726199801665, idp=2.15251425210e-10, iddf=3, idstat=47.97804382236573, sarganp=.0013146751383334, sargandf=2, sargan=13.26833137393004, jp=.0013146751383334, jdf=2, j=13.26833137393004, ll=-335.4059158173529, rankV=13, rankxx=13, rankzz=15, r2c=.2279825291623523, r2u=.9956380713371686, hacsubtitleV="Statistics consistent for homoskedasticity only", hacsubtitleB="Estimates efficient for homoskedasticity only", title="IV (2SLS) estimation", predict="ivreg2_p", version="02.2.08", cmdline="ivreg2 lw expr tenure rns smsa dyear* (s iq=med kww age mrt), small", # noqa:E501 cmd="ivreg2", model="iv", depvar="lw", partialsmall="small", small="small", exexog="med kww age mrt", inexog="expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73", # noqa:E501 insts="med kww age mrt expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73", # noqa:E501 instd="s iq", properties="b V", ) params_table = np.array([ .17242531190423, .02091823230823, 8.2428242197305, 7.570254109e-16, .13135961443277, .2134910093757, 745, 1.9631533327653, 0, -.00909883103476, .00474526917577, -1.917453088061, .055562500369, -.01841452203205, .00021685996252, 745, 1.9631533327653, 0, .04928948974574, .00822542913447, 5.9923304839106, 3.219967401e-09, .03314171112698, .0654372683645, 745, 1.9631533327653, 0, .04221709210309, .00891969338965, 4.7330205488974, 2.647391010e-06, .02470636629795, .05972781790823, 745, 1.9631533327653, 0, -.10179345001799, .03447336568476, -2.9528143828148, .00324794122176, -.16946995275367, -.03411694728232, 745, 1.9631533327653, 0, .12611094946923, .03119614930755, 4.0425165370872, .00005838098525, .06486812498666, .18735377395179, 745, 1.9631533327653, 0, -.05961710621535, .05577581734252, -1.0688701493919, .28547438462206, -.16911358791902, .04987937548832, 745, 1.9631533327653, 0, .04867955999401, .05246796245209, .92779589141575, .35381393703187, -.05432309535722, .15168221534524, 745, 1.9631533327653, 0, .15281763322545, .0520109232025, 2.9381834394759, .00340336072573, .05071221600025, .25492305045064, 745, 1.9631533327653, 0, .17443605148569, .06027671044146, 2.8939212211172, .00391575116799, .0561036264944, .29276847647697, 745, 1.9631533327653, 0, .09166596656323, .05461435829744, 1.678422477547, .09368402236573, -.01555039294523, .19888232607168, 745, 1.9631533327653, 0, .09323976497853, .05767865351978, 1.6165385162217, .10640126701552, -.01999227590824, .20647180586531, 745, 1.9631533327653, 0, 4.0335098946211, .31816162176165, 12.677550083784, 1.723599587e-33, 3.4089098465017, 4.6581099427405, 745, 1.9631533327653, 0]).reshape(13, 9) params_table_colnames = 'b se t pvalue ll ul df crit eform'.split() params_table_rownames = param_names cov = np.array([ .0004375724429, -.00007803749118, .00003449544203, .0000198149417, -.00014459096961, .00002019058603, .00001336734613, -.00010913386703, -.00009506780295, .00003349014856, -.00026522208782, -.00059037802467, .0023409013939, -.00007803749118, .00002251757955, 4.465336774e-06, -8.927380145e-06, .00005957879915, -.00001655549147, -.00001195619881, .00001472288594, -.00001658590268, -.00007381330893, -.00002252874265, .00001745462689, -.00128162250166, .00003449544203, 4.465336774e-06, .00006765768445, -.00001589245933, .00003399284419, -6.395965046e-06, .0000435948215, .00006649766331, .00002517829331, 2.389654105e-06, -.00012997216713, -.00014131961558, -.00098746812283, .0000198149417, -8.927380145e-06, -.00001589245933, .00007956093017, -.00001685000965, -1.479558927e-06, .00006527491612, .00006702237252, .00007894974705, .00006873053302, .00006140852014, .00008063861583, .00049768627977, -.00014459096961, .00005957879915, .00003399284419, -.00001685000965, .00118841294164, .00010083007806, .00010873982024, .00009325616707, -.00004877888958, -.00018456360114, -.00014147359908, -.00017236995151, -.00461299505884, .00002019058603, -.00001655549147, -6.395965046e-06, -1.479558927e-06, .00010083007806, .00097319973162, 2.333629833e-06, -4.248036020e-06, .00008163368686, .00002820282959, .00011655528757, 7.760937245e-06, .00072275206747, .00001336734613, -.00001195619881, .0000435948215, .00006527491612, .00010873982024, 2.333629833e-06, .00311094180023, .00084251830998, .00085523021811, .00088395985737, .00073955611238, .00081093327517, .00006074095578, -.00010913386703, .00001472288594, .00006649766331, .00006702237252, .00009325616707, -4.248036020e-06, .00084251830998, .00275288708387, .00091570803079, .00088036592189, .00076528306455, .00094679625072, -.00115054519119, -.00009506780295, -.00001658590268, .00002517829331, .00007894974705, -.00004877888958, .00008163368686, .00085523021811, .00091570803079, .00270513613238, .00108631323884, .00099102785739, .001210360841, .00183013017674, .00003349014856, -.00007381330893, 2.389654105e-06, .00006873053302, -.00018456360114, .00002820282959, .00088395985737, .00088036592189, .00108631323884, .00363328182164, .00117418056612, .00137311157915, .0060943003149, -.00026522208782, -.00002252874265, -.00012997216713, .00006140852014, -.00014147359908, .00011655528757, .00073955611238, .00076528306455, .00099102785739, .00117418056612, .00298272813224, .00174532225739, .0048868116328, -.00059037802467, .00001745462689, -.00014131961558, .00008063861583, -.00017236995151, 7.760937245e-06, .00081093327517, .00094679625072, .001210360841, .00137311157915, .00174532225739, .00332682707186, .00491674011099, .0023409013939, -.00128162250166, -.00098746812283, .00049768627977, -.00461299505884, .00072275206747, .00006074095578, -.00115054519119, .00183013017674, .0060943003149, .0048868116328, .00491674011099, .101226817562]).reshape(13, 13) cov_colnames = param_names cov_rownames = param_names # not autogenerated # calculated with `ivendog` after ivreg2 hausman = dict( df_r=743, df=2, WHFp=1.47099195224e-16, WHF=38.30408858936179, DWHp=4.12270104038e-16, DWH=70.84970589405181 ) results_small = 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, hausman=hausman, **est ) est = dict( N=758, inexog_ct=10, exexog_ct=4, endog_ct=2, partial_ct=0, df_r=745, df_m=12, sdofminus=0, dofminus=0, r2=.2279825291623523, rmse=.3799175840045295, rss=107.5313411236999, mss=31.75480871825532, r2_a=.2155473484240278, F=40.08955571761724, Fp=1.50331141073e-72, Fdf1=12, Fdf2=745, yy=24652.2466174172, yyc=139.2861498419552, partialcons=0, cons=1, cdf=12.55161416131593, widstat=11.46142788662503, cd=.0675726199801665, idp=6.77658650925e-09, iddf=3, idstat=40.92698219921901, jp=.0030253131145893, jdf=2, j=11.60148136780177, ll=-335.4059158173529, rankV=13, rankS=15, rankxx=13, rankzz=15, r2c=.2279825291623523, r2u=.9956380713371686, hacsubtitleV="Statistics robust to heteroskedasticity", hacsubtitleB="Estimates efficient for homoskedasticity only", title="IV (2SLS) estimation", predict="ivreg2_p", version="02.2.08", cmdline="ivreg2 lw expr tenure rns smsa dyear* (s iq=med kww age mrt), small robust", # noqa:E501 cmd="ivreg2", model="iv", depvar="lw", vcetype="Robust", partialsmall="small", small="small", exexog="med kww age mrt", inexog="expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73", # noqa:E501 insts="med kww age mrt expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73", # noqa:E501 instd="s iq", properties="b V", ) params_table = np.array([ .17242531190423, .02091963554158, 8.2422713130704, 7.602390656e-16, .13135685967055, .21349376413792, 745, 1.9631533327653, 0, -.00909883103476, .00492868646034, -1.8460965427549, .06527471189517, -.01877459828554, .00057693621602, 745, 1.9631533327653, 0, .04928948974574, .00811972711081, 6.0703382112603, 2.032123371e-09, .03334922040701, .06522975908447, 745, 1.9631533327653, 0, .04221709210309, .0095458460509, 4.4225615915015, .00001120706314, .0234771326142, .06095705159197, 745, 1.9631533327653, 0, -.10179345001799, .0340033743578, -2.9936278954812, .00284801771574, -.16854728771377, -.03503961232222, 745, 1.9631533327653, 0, .12611094946923, .03107904208149, 4.057748920915, .00005477891606, .0650980244278, .18712387451065, 745, 1.9631533327653, 0, -.05961710621535, .05216296564028, -1.1429010119263, .25344683532742, -.16202100605898, .04278679362828, 745, 1.9631533327653, 0, .04867955999401, .05024595634484, .96882542467533, .33294671206223, -.04996095666234, .14732007665036, 745, 1.9631533327653, 0, .15281763322545, .04834485710231, 3.1609904834768, .00163599246861, .05790926588299, .24772600056791, 745, 1.9631533327653, 0, .17443605148569, .06165614610562, 2.8291753945643, .00479228267652, .05339558277297, .2954765201984, 745, 1.9631533327653, 0, .09166596656323, .05594360469515, 1.638542368922, .10173082236229, -.01815990744096, .20149184056742, 745, 1.9631533327653, 0, .09323976497853, .06137760691084, 1.5191169820938, .12915730016675, -.02725388858564, .21373341854271, 745, 1.9631533327653, 0, 4.0335098946211, .33794335658841, 11.935461419748, 3.577808889e-30, 3.3700752678487, 4.6969445213936, 745, 1.9631533327653, 0]).reshape(13, 9) params_table_colnames = 'b se t pvalue ll ul df crit eform'.split() params_table_rownames = param_names cov = np.array([ .00043763115119, -.00007958438537, .00002863778986, .00001496321982, -.00018453787082, -8.550864865e-06, -.0001185286492, -.00019431868638, -.00012891699672, -.00008824260285, -.00031897080643, -.0006359997119, .00263218578127, -.00007958438537, .00002429195022, 4.997376715e-06, -.00001117849889, .00006733963701, -.00002196338101, 8.249019125e-07, .00003312119623, -.0000218044746, -.00006079620995, -.00001426851928, .00001410067206, -.00145119299411, .00002863778986, 4.997376715e-06, .00006592996835, -.00002012308078, .00005199457086, -.00003289221373, .00003620055764, .00008731531776, .00003013530738, .00001504503258, -.00008879845405, -.00013220917123, -.00095762480745, .00001496321982, -.00001117849889, -.00002012308078, .00009112317683, -.000038508438, 7.182733576e-06, .00008294199433, .00006458818434, .00011041870299, .00009792126329, .00011012658665, .00009292324197, .00076092104218, -.00018453787082, .00006733963701, .00005199457086, -.000038508438, .00115622946772, .00013545834604, .00019371742478, .000203588175, 7.317274626e-06, .00002369735603, -.00005176761775, .00009245129039, -.00504399373059, -8.550864865e-06, -.00002196338101, -.00003289221373, 7.182733576e-06, .00013545834604, .0009659068567, .00006303558567, .00012016301635, .00025950456399, .00011274488624, .00030660513471, .00031212041314, .00158010341555, -.0001185286492, 8.249019125e-07, .00003620055764, .00008294199433, .00019371742478, .00006303558567, .00272097498439, .0008763833685, .000936230107, .00097234969671, .0009024844391, .00104679250917, .00032228914613, -.00019431868638, .00003312119623, .00008731531776, .00006458818434, .000203588175, .00012016301635, .0008763833685, .00252465612901, .00094650107881, .0008930540954, .00082494813305, .00095933828863, -.00210700630956, -.00012891699672, -.0000218044746, .00003013530738, .00011041870299, 7.317274626e-06, .00025950456399, .000936230107, .00094650107881, .00233722520824, .0011975566966, .00111394872039, .00131507937389, .00254447447521, -.00008824260285, -.00006079620995, .00001504503258, .00009792126329, .00002369735603, .00011274488624, .00097234969671, .0008930540954, .0011975566966, .0038014803526, .00125650121313, .00157186205549, .00611293275102, -.00031897080643, -.00001426851928, -.00008879845405, .00011012658665, -.00005176761775, .00030660513471, .0009024844391, .00082494813305, .00111394872039, .00125650121313, .00312968690629, .00184349552167, .0043840308983, -.0006359997119, .00001410067206, -.00013220917123, .00009292324197, .00009245129039, .00031212041314, .00104679250917, .00095933828863, .00131507937389, .00157186205549, .00184349552167, .0037672106301, .00544245897816, .00263218578127, -.00145119299411, -.00095762480745, .00076092104218, -.00504399373059, .00158010341555, .00032228914613, -.00210700630956, .00254447447521, .00611293275102, .0043840308983, .00544245897816, .11420571226224]).reshape(13, 13) cov_colnames = param_names cov_rownames = param_names results_small_robust = 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 ) est = dict( N=758, inexog_ct=10, exexog_ct=4, endog_ct=2, partial_ct=0, df_m=12, sdofminus=0, dofminus=0, r2=.2168305947462866, rmse=.3793562300139549, rss=109.0846511318037, mss=30.20149871015154, r2_a=.2042157855341463, F=41.97598961240392, Fp=1.89290505854e-75, Fdf1=12, Fdf2=745, yy=24652.2466174172, yyc=139.2861498419552, r2u=.995575062475047, partialcons=0, cons=1, cdf=12.55161416131593, widstat=11.46142788662503, cd=.0675726199801665, idp=6.77658650925e-09, iddf=3, idstat=40.92698219921901, jp=.0030253131145893, jdf=2, j=11.60148136780177, ll=-340.8414755627023, rankV=13, rankS=15, rankxx=13, rankzz=15, r2c=.2168305947462866, hacsubtitleV="Statistics robust to heteroskedasticity", hacsubtitleB="Estimates efficient for arbitrary heteroskedasticity", title="2-Step GMM estimation", predict="ivreg2_p", version="02.2.08", cmdline="ivreg2 lw expr tenure rns smsa dyear* (s iq=med kww age mrt), gmm2s robust", # noqa:E501 cmd="ivreg2", model="gmm2s", depvar="lw", vcetype="Robust", partialsmall="small", exexog="med kww age mrt", inexog="expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73", # noqa:E501 insts="med kww age mrt expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73", # noqa:E501 instd="s iq", properties="b V", ) params_table = np.array([ .17579576800916, .02067662557145, 8.5021498020418, 1.861114268e-17, .13527032656731, .21632120945101, np.nan, 1.9599639845401, 0, -.00928615655484, .00488241935929, -1.9019579989933, .05717664576009, -.01885552265647, .00028320954679, np.nan, 1.9599639845401, 0, .0502827590727, .00804384217519, 6.251087226427, 4.076051363e-10, .03451711811201, .06604840003339, np.nan, 1.9599639845401, 0, .04252138311207, .00945488880069, 4.4972906618407, 6.882486994e-06, .0239901415849, .06105262463925, np.nan, 1.9599639845401, 0, -.10409306761385, .03352385821237, -3.105044382255, .00190250475151, -.16979862233293, -.03838751289477, np.nan, 1.9599639845401, 0, .12475123235604, .03077474836143, 4.0536881371349, .00005041641801, .06443383393436, .18506863077773, np.nan, 1.9599639845401, 0, -.05304317352459, .05146091261443, -1.0307468490116, .30265954893659, -.15390470886044, .04781836181126, np.nan, 1.9599639845401, 0, .04595459037414, .04957352345681, .92699867126001, .35392722417214, -.05120773018796, .14311691093625, np.nan, 1.9599639845401, 0, .15548006234452, .04763105506, 3.264258206094, .00109751095685, .06212490988128, .24883521480777, np.nan, 1.9599639845401, 0, .16698745539298, .06100058345996, 2.7374730850337, .00619131861175, .04742850877554, .28654640201043, np.nan, 1.9599639845401, 0, .08464846645323, .05540348923564, 1.5278544297672, .12654868468826, -.02394037706648, .19323730997294, np.nan, 1.9599639845401, 0, .09960684400937, .06070338085689, 1.6408780302402, .10082273637942, -.01936959620995, .2185832842287, np.nan, 1.9599639845401, 0, 4.0039243729942, .33484233500232, 11.957640819123, 5.922056626e-33, 3.3476454558904, 4.6602032900981, np.nan, 1.9599639845401, 0]).reshape(13, 9) params_table_colnames = 'b se z pvalue ll ul df crit eform'.split() params_table_rownames = param_names cov = np.array([ .00042752284502, -.00007792910934, .00002799053689, .00001411255161, -.00017627354771, -6.164167361e-06, -.00011238067476, -.0001840592946, -.00012075971225, -.00008045016526, -.00030956302647, -.00062213915849, .0025864317125, -.00007792910934, .0000238380188, 4.908457545e-06, -.0000109083863, .00006550360735, -.00002187678826, 4.129956698e-08, .00003161755605, -.00002236410253, -.00006047183643, -.0000144130349, .00001326368633, -.00142544425011, .00002799053689, 4.908457545e-06, .00006470339694, -.00001976276105, .00005093819277, -.0000323622135, .00003452531659, .00008555059005, .0000288422901, .00001508898973, -.00008682961428, -.00013087868262, -.00093791708189, .00001411255161, -.0000109083863, -.00001976276105, .00008939492223, -.00003640760589, 7.669817608e-06, .00008322349071, .00006546263505, .00011054793547, .00009771882719, .00010901321979, .0000926582973, .00074577978354, -.00017627354771, .00006550360735, .00005093819277, -.00003640760589, .00112384906944, .00012782990916, .0001752536517, .00018281146997, -.00001062129812, .0000105829103, -.00005745271672, .00007901951626, -.00493822443862, -6.164167361e-06, -.00002187678826, -.0000323622135, 7.669817608e-06, .00012782990916, .00094708513671, .00005587818268, .0001108109152, .00024772966236, .00010526192189, .00029836672951, .0003020559345, .00155999842113, -.00011238067476, 4.129956698e-08, .00003452531659, .00008322349071, .0001752536517, .00005587818268, .00264822552711, .00084013008538, .00089372787896, .00094462335409, .00088393735147, .00100740924129, .00036769011237, -.0001840592946, .00003161755605, .00008555059005, .00006546263505, .00018281146997, .0001108109152, .00084013008538, .00245753422792, .00090550111616, .00086043845508, .00080197738555, .00092623266791, -.00204303139567, -.00012075971225, -.00002236410253, .0000288422901, .00011054793547, -.00001062129812, .00024772966236, .00089372787896, .00090550111616, .00226871740613, .00116169566043, .00108859648529, .00127118636737, .00254572878148, -.00008045016526, -.00006047183643, .00001508898973, .00009771882719, .0000105829103, .00010526192189, .00094462335409, .00086043845508, .00116169566043, .00372107118246, .00122563410541, .00153681917159, .00601243235865, -.00030956302647, -.0000144130349, -.00008682961428, .00010901321979, -.00005745271672, .00029836672951, .00088393735147, .00080197738555, .00108859648529, .00122563410541, .00306954661948, .00181011399651, .00430171295221, -.00062213915849, .00001326368633, -.00013087868262, .0000926582973, .00007901951626, .0003020559345, .00100740924129, .00092623266791, .00127118636737, .00153681917159, .00181011399651, .00368490044746, .00539258837124, .0025864317125, -.00142544425011, -.00093791708189, .00074577978354, -.00493822443862, .00155999842113, .00036769011237, -.00204303139567, .00254572878148, .00601243235865, .00430171295221, .00539258837124, .11211938930981]).reshape(13, 13) cov_colnames = param_names cov_rownames = param_names results_gmm2s_robust = 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 )