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/ regression / tests / results / results_grunfeld_ols_robust_cluster.py
import numpy as np
from statsmodels.tools.testing import ParamsTableTestBunch
est = dict(
N_clust=10,
N=200,
df_m=2,
df_r=9,
F=51.59060716590177,
r2=.8124080178314147,
rmse=94.40840193979599,
mss=7604093.484267689,
rss=1755850.432294737,
r2_a=.8105035307027997,
ll=-1191.80235741801,
ll_0=-1359.150955647688,
rank=3,
cmdline="regress invest mvalue kstock, vce(cluster company)",
title="Linear regression",
marginsok="XB default",
vce="cluster",
depvar="invest",
cmd="regress",
properties="b V",
predict="regres_p",
model="ols",
estat_cmd="regress_estat",
vcetype="Robust",
clustvar="company",
)
params_table = np.array([
.11556215606596, .01589433647768, 7.2706499090564, .00004710548549,
.07960666895505, .15151764317688, 9, 2.2621571627982,
0, .23067848754982, .08496711097464, 2.7149150406994,
.02380515903536, .03846952885627, .42288744624337, 9,
2.2621571627982, 0, -42.714369016733, 20.425202580078,
-2.0912580352272, .06604843284516, -88.919387334862, 3.4906493013959,
9, 2.2621571627982, 0]).reshape(3, 9)
params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
params_table_rownames = 'mvalue kstock _cons'.split()
cov = np.array([
.00025262993207, -.00065043385106, .20961897960949, -.00065043385106,
.00721940994738, -1.2171040967615, .20961897960949, -1.2171040967615,
417.18890043724]).reshape(3, 3)
cov_colnames = 'mvalue kstock _cons'.split()
cov_rownames = 'mvalue kstock _cons'.split()
results_cluster = 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=200,
inexog_ct=2,
exexog_ct=0,
endog_ct=0,
partial_ct=0,
N_clust=10,
df_m=2,
sdofminus=0,
dofminus=0,
r2=.8124080178314146,
rmse=93.69766358599176,
rss=1755850.432294737,
mss=7604093.484267682,
r2_a=.8105035307027995,
F=51.59060716590192,
Fp=.0000117341240941,
Fdf1=2,
Fdf2=9,
yy=13620706.07273678,
yyc=9359943.916562419,
partialcons=0,
cons=1,
jdf=0,
j=0,
ll=-1191.802357418011,
rankV=3,
rankS=3,
rankxx=3,
rankzz=3,
r2c=.8124080178314146,
r2u=.8710896173136538,
clustvar="company",
hacsubtitleV="Statistics robust to heteroskedasticity and clustering on company", # noqa:E501
hacsubtitleB="Estimates efficient for homoskedasticity only",
title="OLS estimation",
predict="ivreg2_p",
version="03.1.07",
cmdline="ivreg2 invest mvalue kstock, cluster(company)",
cmd="ivreg2",
model="ols",
depvar="invest",
vcetype="Robust",
vce="robust cluster",
partialsmall="small",
inexog="mvalue kstock",
insts="mvalue kstock",
properties="b V",
)
params_table = np.array([
.11556215606596, .01500272788516, 7.7027429245215, 1.331761148e-14,
.08615734974119, .14496696239074, np.nan, 1.9599639845401,
0, .23067848754982, .08020079648691, 2.8762618035529,
.00402415789383, .07348781490405, .38786916019559, np.nan,
1.9599639845401, 0, -42.714369016733, 19.27943055305,
-2.2155410088072, .02672295281194, -80.501358543152, -4.9273794903145,
np.nan, 1.9599639845401, 0]).reshape(3, 9)
params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
params_table_rownames = 'mvalue kstock _cons'.split()
cov = np.array([
.000225081844, -.00057950714469, .1867610305767, -.00057950714469,
.00643216775713, -1.0843847053056, .1867610305767, -1.0843847053056,
371.69644244987]).reshape(3, 3)
cov_colnames = 'mvalue kstock _cons'.split()
cov_rownames = 'mvalue kstock _cons'.split()
results_cluster_large = 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=200,
N_g=10,
df_m=2,
df_r=9,
F=97.97910905239282,
r2=.8124080178314147,
rmse=94.40840193979599,
lag=4,
cmd="xtscc",
predict="xtscc_p",
method="Pooled OLS",
depvar="invest",
vcetype="Drisc/Kraay",
title="Regression with Driscoll-Kraay standard errors",
groupvar="company",
properties="b V",
)
params_table = np.array([
.11556215606596, .0134360177573, 8.6009231420662, .00001235433261,
.08516777225681, .14595653987512, 9, 2.2621571627982,
0, .23067848754982, .04930800664089, 4.678317037431,
.00115494570515, .11913602714384, .3422209479558, 9,
2.2621571627982, 0, -42.714369016733, 12.190347184209,
-3.5039501641153, .0066818746948, -70.290850216489, -15.137887816977,
9, 2.2621571627982, 0]).reshape(3, 9)
params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
params_table_rownames = 'mvalue kstock _cons'.split()
cov = np.array([
.00018052657317, -.00035661054613, -.06728261073866, -.00035661054613,
.0024312795189, -.32394785247278, -.06728261073866, -.32394785247278,
148.60456447156]).reshape(3, 3)
cov_colnames = 'mvalue kstock _cons'.split()
cov_rownames = 'mvalue kstock _cons'.split()
results_nw_groupsum4 = 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(
df_m=2,
df_r=197,
F=73.07593045506036,
N=200,
lag=4,
rank=3,
title="Regression with Newey-West standard errors",
cmd="newey",
cmdline="newey invest mvalue kstock, lag(4) force",
estat_cmd="newey_estat",
predict="newey_p",
vcetype="Newey-West",
depvar="invest",
properties="b V",
)
params_table = np.array([
.11556215606596, .01142785251475, 10.112324771147, 1.251631065e-19,
.0930255277205, .13809878441142, 197, 1.9720790337785,
0, .23067848754982, .06842168281423, 3.3714237660029,
.00089998163666, .09574552141602, .36561145368361, 197,
1.9720790337785, 0, -42.714369016733, 16.179042041128,
-2.6401049523298, .00895205094219, -74.620718612662, -10.808019420804,
197, 1.9720790337785, 0]).reshape(3, 9)
params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
params_table_rownames = 'mvalue kstock _cons'.split()
cov = np.array([
.0001305958131, -.00022910455176, .00889686530849, -.00022910455176,
.00468152667913, -.88403667445531, .00889686530849, -.88403667445531,
261.76140136858]).reshape(3, 3)
cov_colnames = 'mvalue kstock _cons'.split()
cov_rownames = 'mvalue kstock _cons'.split()
results_nw_panel4 = 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=200,
inexog_ct=2,
exexog_ct=0,
endog_ct=0,
partial_ct=0,
df_r=9,
N_clust=10,
N_clust1=10,
N_clust2=20,
df_m=2,
sdofminus=0,
dofminus=0,
r2=.8124080178314146,
rmse=94.40840193979601,
rss=1755850.432294737,
mss=7604093.484267682,
r2_a=.8105035307027995,
F=57.99124535923564,
Fp=7.21555935862e-06,
Fdf1=2,
partialcons=0,
cons=1,
jdf=0,
j=0,
ll=-1191.802357418011,
rankV=3,
rankS=3,
rankxx=3,
rankzz=3,
r2c=.8124080178314146,
r2u=.8710896173136538,
yyc=9359943.916562419,
yy=13620706.07273678,
Fdf2=9,
clustvar="company time",
hacsubtitleV="Statistics robust to heteroskedasticity and clustering on company and time", # noqa:E501
hacsubtitleB="Estimates efficient for homoskedasticity only",
title="OLS estimation",
predict="ivreg2_p",
version="03.1.07",
cmdline="ivreg2 invest mvalue kstock, cluster(company time) small",
cmd="ivreg2",
model="ols",
depvar="invest",
vcetype="Robust",
clustvar2="time",
clustvar1="company",
vce="robust two-way cluster",
partialsmall="small",
small="small",
inexog="mvalue kstock",
insts="mvalue kstock",
properties="b V",
)
params_table = np.array([
.11556215606596, .01635175387097, 7.0672636695645, .00005873628221,
.07857191892244, .15255239320949, 9, 2.2621571627982,
0, .23067848754982, .07847391274682, 2.9395563375824,
.01649863150032, .05315816373679, .40819881136285, 9,
2.2621571627982, 0, -42.714369016733, 19.505607409785,
-2.189850750062, .05626393734425, -86.839118533508, 1.4103805000422,
9, 2.2621571627982, 0]).reshape(3, 9)
params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
params_table_rownames = 'mvalue kstock _cons'.split()
cov = np.array([
.00026737985466, -.00070163493529, .19641438763743, -.00070163493529,
.0061581549818, -.99627581152391, .19641438763743, -.99627581152391,
380.46872042467]).reshape(3, 3)
cov_colnames = 'mvalue kstock _cons'.split()
cov_rownames = 'mvalue kstock _cons'.split()
results_cluster_2groups_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,
**est
)
est = dict(
N=200,
inexog_ct=2,
exexog_ct=0,
endog_ct=0,