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/ regression / tests / results / results_macro_ols_robust.py
'''autogenerated and edited by hand
'''
import numpy as np
from statsmodels.tools.testing import ParamsTableTestBunch
est = dict(
N=202,
df_m=2,
df_r=199,
F=92.94502024547633,
r2=.6769775594319385,
rmse=10.7037959322668,
mss=47782.65712176046,
rss=22799.67822456265,
r2_a=.6737311027428123,
ll=-763.9752181602238,
ll_0=-878.1085999159409,
rank=3,
cmdline="regress g_realinv g_realgdp L.realint, vce(robust)",
title="Linear regression",
marginsok="XB default",
vce="robust",
depvar="g_realinv",
cmd="regress",
properties="b V",
predict="regres_p",
model="ols",
estat_cmd="regress_estat",
vcetype="Robust",
)
params_table = np.array([
4.3742216647032, .32355452428856, 13.519272136038, 5.703151404e-30,
3.7361862031101, 5.0122571262963, 199, 1.9719565442518,
0, -.61399696947899, .32772840315987, -1.8734933059173,
.06246625509181, -1.2602631388273, .0322691998693, 199,
1.9719565442518, 0, -9.4816727746549, 1.3690593206013,
-6.9256843965613, 5.860240898e-11, -12.181398261383, -6.7819472879264,
199, 1.9719565442518, 0]).reshape(3, 9)
params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
params_table_rownames = 'g_realgdp L.realint _cons'.split()
cov = np.array([
.1046875301876, -.00084230205782, -.34205013876828, -.00084230205782,
.10740590623772, -.14114426417778, -.34205013876828, -.14114426417778,
1.8743234233252]).reshape(3, 3)
cov_colnames = 'g_realgdp L.realint _cons'.split()
cov_rownames = 'g_realgdp L.realint _cons'.split()
results_hc0 = 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=199,
F=89.45120275471848,
N=202,
lag=4,
rank=3,
title="Regression with Newey-West standard errors",
cmd="newey",
cmdline="newey g_realinv g_realgdp L.realint, lag(4)",
estat_cmd="newey_estat",
predict="newey_p",
vcetype="Newey-West",
depvar="g_realinv",
properties="b V",
)
params_table = np.array([
4.3742216647032, .33125644884286, 13.204940401864, 5.282334606e-29,
3.7209983425819, 5.0274449868245, 199, 1.9719565442518,
0, -.61399696947899, .29582347593197, -2.0755518727668,
.03922090940364, -1.1973480087863, -.03064593017165, 199,
1.9719565442518, 0, -9.4816727746549, 1.1859338087713,
-7.9951112823729, 1.036821797e-13, -11.820282709911, -7.1430628393989,
199, 1.9719565442518, 0]).reshape(3, 9)
params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
params_table_rownames = 'g_realgdp L.realint _cons'.split()
cov = np.array([
.10973083489998, .0003953117603, -.31803287070833, .0003953117603,
.08751152891247, -.06062111121649, -.31803287070833, -.06062111121649,
1.4064389987868]).reshape(3, 3)
cov_colnames = 'g_realgdp L.realint _cons'.split()
cov_rownames = 'g_realgdp L.realint _cons'.split()
results_newey4 = 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=202,
inexog_ct=2,
exexog_ct=0,
endog_ct=0,
partial_ct=0,
bw=5,
df_r=199,
df_m=2,
sdofminus=0,
dofminus=0,
r2=.6769775594319388,
rmse=10.7037959322668,
rss=22799.67822456265,
mss=47782.65712176055,
r2_a=.6737311027428126,
F=89.45120275471867,
Fp=1.93466284646e-28,
Fdf1=2,
Fdf2=199,
yy=72725.68049533673,
partialcons=0,
cons=1,
jdf=0,
j=0,
ll=-763.9752181602239,
rankV=3,
rankS=3,
rankxx=3,
rankzz=3,
r2c=.6769775594319388,
r2u=.6864975608440735,
yyc=70582.33534632321,
hacsubtitleV="Statistics robust to heteroskedasticity and autocorrelation",
hacsubtitleB="Estimates efficient for homoskedasticity only",
title="OLS estimation",
predict="ivreg2_p",
version="02.2.08",
cmdline="ivreg2 g_realinv g_realgdp L.realint, robust bw(5) small",
cmd="ivreg2",
model="ols",
depvar="g_realinv",
vcetype="Robust",
partialsmall="small",
small="small",
tvar="qu",
kernel="Bartlett",
inexog="g_realgdp L.realint",
insts="g_realgdp L.realint",
properties="b V",
)
params_table = np.array([
4.3742216647032, .33125644884286, 13.204940401864, 5.282334606e-29,
3.7209983425819, 5.0274449868245, 199, 1.9719565442518,
0, -.61399696947899, .29582347593197, -2.0755518727668,
.03922090940364, -1.1973480087863, -.03064593017165, 199,
1.9719565442518, 0, -9.4816727746549, 1.1859338087713,
-7.9951112823729, 1.036821797e-13, -11.820282709911, -7.1430628393989,
199, 1.9719565442518, 0]).reshape(3, 9)
params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
params_table_rownames = 'g_realgdp L.realint _cons'.split()
cov = np.array([
.10973083489998, .0003953117603, -.31803287070833, .0003953117603,
.08751152891247, -.06062111121649, -.31803287070833, -.06062111121649,
1.4064389987868]).reshape(3, 3)
cov_colnames = 'g_realgdp L.realint _cons'.split()
cov_rownames = 'g_realgdp L.realint _cons'.split()
results_ivhac4_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=202,
inexog_ct=2,
exexog_ct=0,
endog_ct=0,
partial_ct=0,
bw=5,
df_m=2,
sdofminus=0,
dofminus=0,
r2=.6769775594319388,
rmse=10.6240149746225,
rss=22799.67822456265,
mss=47782.65712176055,
r2_a=.6737311027428126,
F=89.45120275471867,
Fp=1.93466284646e-28,
Fdf1=2,
Fdf2=199,
yy=72725.68049533673,
yyc=70582.33534632321,
partialcons=0,
cons=1,
jdf=0,
j=0,
ll=-763.9752181602239,
rankV=3,
rankS=3,
rankxx=3,
rankzz=3,
r2c=.6769775594319388,
r2u=.6864975608440735,
hacsubtitleV="Statistics robust to heteroskedasticity and autocorrelation",
hacsubtitleB="Estimates efficient for homoskedasticity only",
title="OLS estimation",
predict="ivreg2_p",
version="02.2.08",
cmdline="ivreg2 g_realinv g_realgdp L.realint, robust bw(5)",
cmd="ivreg2",
model="ols",
depvar="g_realinv",
vcetype="Robust",
partialsmall="small",
tvar="qu",
kernel="Bartlett",
inexog="g_realgdp L.realint",
insts="g_realgdp L.realint",
properties="b V",
)
params_table = np.array([
4.3742216647032, .32878742225811, 13.304102798888, 2.191074740e-40,
3.7298101585076, 5.0186331708989, np.nan, 1.9599639845401,
0, -.61399696947899, .29361854972141, -2.0911382133777,
.03651567605333, -1.1894787521258, -.03851518683214, np.nan,
1.9599639845401, 0, -9.4816727746549, 1.1770944273439,
-8.055150508231, 7.938107001e-16, -11.788735458652, -7.1746100906581,
np.nan, 1.9599639845401, 0]).reshape(3, 9)
params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
params_table_rownames = 'g_realgdp L.realint _cons'.split()
cov = np.array([
.10810116903513, .00038944079356, -.31330961025227, .00038944079356,
.0862118527405, -.05972079768357, -.31330961025227, -.05972079768357,
1.385551290884]).reshape(3, 3)
cov_colnames = 'g_realgdp L.realint _cons'.split()
cov_rownames = 'g_realgdp L.realint _cons'.split()
results_ivhac4_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=202,
inexog_ct=2,
exexog_ct=0,
endog_ct=0,
partial_ct=0,
df_r=199,
df_m=2,
sdofminus=0,
dofminus=0,
r2=.6769775594319388,
rmse=10.7037959322668,
rss=22799.67822456265,
mss=47782.65712176055,
r2_a=.6737311027428126,
F=92.94502024547634,
Fp=3.12523087723e-29,
Fdf1=2,
Fdf2=199,
yy=72725.68049533673,
yyc=70582.33534632321,
partialcons=0,
cons=1,
jdf=0,
j=0,
ll=-763.9752181602239,
rankV=3,
rankS=3,
rankxx=3,
rankzz=3,
r2c=.6769775594319388,
r2u=.6864975608440735,
hacsubtitleV="Statistics robust to heteroskedasticity",
hacsubtitleB="Estimates efficient for homoskedasticity only",
title="OLS estimation",
predict="ivreg2_p",
version="02.2.08",
cmdline="ivreg2 g_realinv g_realgdp L.realint, robust small",
cmd="ivreg2",
model="ols",
depvar="g_realinv",
vcetype="Robust",
partialsmall="small",
small="small",
inexog="g_realgdp L.realint",
insts="g_realgdp L.realint",
properties="b V",
)
params_table = np.array([
4.3742216647032, .32355452428856, 13.519272136038, 5.703151404e-30,
3.7361862031101, 5.0122571262963, 199, 1.9719565442518,
0, -.61399696947899, .32772840315987, -1.8734933059173,
.06246625509181, -1.2602631388273, .0322691998693, 199,
1.9719565442518, 0, -9.4816727746549, 1.3690593206013,
-6.9256843965613, 5.860240898e-11, -12.181398261383, -6.7819472879264,
199, 1.9719565442518, 0]).reshape(3, 9)
params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()