'''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()

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_ivhc0_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,
    df_m=2,
    sdofminus=0,
    dofminus=0,
    r2=.6769775594319388,
    rmse=10.6240149746225,
    rss=22799.67822456265,
    mss=47782.65712176055,
    r2_a=.6737311027428126,
    F=92.94502024547633,
    Fp=3.12523087723e-29,
    Fdf1=2,
    Fdf2=199,
    yy=72725.68049533673,
    yyc=70582.33534632321,
    r2u=.6864975608440735,
    partialcons=0,
    cons=1,
    jdf=0,
    j=0,
    ll=-763.9752181602239,
    rankV=3,
    rankS=3,
    rankxx=3,
    rankzz=3,
    r2c=.6769775594319388,
    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",
    cmd="ivreg2",
    model="ols",
    depvar="g_realinv",
    vcetype="Robust",
    partialsmall="small",
    inexog="g_realgdp L.realint",
    insts="g_realgdp L.realint",
    properties="b V",
)

params_table = np.array([
    4.3742216647032,  .32114290415293,  13.620795004769,  3.012701837e-42,
    3.7447931386729,  5.0036501907336, np.nan,  1.9599639845401,
    0, -.61399696947899,  .32528567293437, -1.8875622892954,
    .05908473670106, -1.2515451731172,  .02355123415926, np.nan,
    1.9599639845401,                0, -9.4816727746549,  1.3588550094989,
    -6.9776927695558,  3.000669464e-12, -12.144979653484, -6.8183658958253,
    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([
    .10313276488778, -.00082979262132, -.33697018621231, -.00082979262132,
    .10581076901637, -.13904806223455, -.33697018621231, -.13904806223455,
    1.8464869368401]).reshape(3, 3)

cov_colnames = 'g_realgdp L.realint _cons'.split()

cov_rownames = 'g_realgdp L.realint _cons'.split()

results_ivhc0_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
)