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import numpy as np
from statsmodels.tools.testing import ParamsTableTestBunch
est = dict(
mlag_var=3,
fpe_var=7.47593408072e-13,
aic_var=-19.4085401106207,
hqic_var=-19.20760258859687,
sbic_var=-18.91206199634062,
tparms_var=30,
k_var=30,
df_eq_var=10,
k_aux=18,
k_eq=18,
k_eq_var=3,
k_dv_var=3,
neqs_var=3,
k_dv=3,
neqs=3,
N_cns=12,
ic_ml=9,
rc_ml=0,
oid_df=0,
N=199,
rank=6,
F_3_var=9.018236399703298,
df_r3_var=189,
df_m3_var=9,
ll_3_var=364.1156201942387,
r2_3_var=.3004252574875596,
rmse_3_var=.0398398997327694,
k_3_var=10,
obs_3_var=199,
F_2_var=5.002566602091567,
df_r2_var=189,
df_m2_var=9,
ll_2_var=728.0001662442413,
r2_2_var=.1923874161594955,
rmse_2_var=.0064000343524738,
k_2_var=10,
obs_2_var=199,
F_1_var=8.356742395485949,
df_r1_var=189,
df_m1_var=9,
ll_1_var=694.4411801251371,
r2_1_var=.2846617748967589,
rmse_1_var=.0075756675969815,
k_1_var=10,
obs_1_var=199,
df_r=193,
df_r_var=189,
ll=1945.759734821802,
ll_var=1961.149741006759,
detsig_ml_var=5.52855987611e-13,
detsig_var=6.45335912865e-13,
T_var=199,
N_gaps_var=0,
tmin=0,
tmax=198,
cmd="svar",
cmdline="svar gdp cons inv, aeq(A) beq(B) lags(1/3) var dfk small",
predict="svar_p",
dfk_var="dfk",
vcetype="EIM",
lags_var="1 2 3",
depvar_var="gdp cons inv",
eqnames_var="gdp cons inv",
endog_var="gdp cons inv",
timevar="qtrdate",
tsfmt="%tq",
small="small",
title="Structural vector autoregression",
cns_b="[b_1_2]_cons = 0:[b_1_3]_cons = 0:[b_2_1]_cons = 0:[b_2_3]_cons = 0:[b_3_1]_cons = 0:[b_3_2]_cons = 0", # noqa:E501
cns_a="[a_1_1]_cons = 1:[a_1_2]_cons = 0:[a_1_3]_cons = 0:[a_2_2]_cons = 1:[a_2_3]_cons = 0:[a_3_3]_cons = 1", # noqa:E501
properties="b V",
)
params_table = np.array([
1, np.nan, np.nan, np.nan,
np.nan, np.nan, 193, 1.9723316757957,
0, -.50680224519119, .04791445158754, -10.577231469827,
6.466439125e-21, -.60130543578568, -.41229905459671, 193,
1.9723316757957, 0, -5.5360565201616, .24220266982262,
-22.857124259679, 8.232580974e-57, -6.013760517815, -5.0583525225081,
193, 1.9723316757957, 0, 0,
np.nan, np.nan, np.nan, np.nan,
np.nan, 193, 1.9723316757957, 0,
1, np.nan, np.nan, np.nan,
np.nan, np.nan, 193, 1.9723316757957,
0, 3.0411768648574, .28669329203947, 10.607771263929,
5.260805180e-21, 2.4757226037298, 3.606631125985, 193,
1.9723316757957, 0, 0, np.nan,
np.nan, np.nan, np.nan, np.nan,
193, 1.9723316757957, 0, 0,
np.nan, np.nan, np.nan, np.nan,
np.nan, 193, 1.9723316757957, 0,
1, np.nan, np.nan, np.nan,
np.nan, np.nan, 193, 1.9723316757957,
0, .00757566759698, .00037973390425, 19.94993734326,
8.739086225e-49, .00682670638925, .00832462880472, 193,
1.9723316757957, 0, 0, np.nan,
np.nan, np.nan, np.nan, np.nan,
193, 1.9723316757957, 0, 0,
np.nan, np.nan, np.nan, np.nan,
np.nan, 193, 1.9723316757957, 0,
0, np.nan, np.nan, np.nan,
np.nan, np.nan, 193, 1.9723316757957,
0, .00512051886486, .00025666841839, 19.94993734326,
8.739086225e-49, .00461428361309, .00562675411662, 193,
1.9723316757957, 0, 0, np.nan,
np.nan, np.nan, np.nan, np.nan,
193, 1.9723316757957, 0, 0,
np.nan, np.nan, np.nan, np.nan,
np.nan, 193, 1.9723316757957, 0,
0, np.nan, np.nan, np.nan,
np.nan, np.nan, 193, 1.9723316757957,
0, .02070894812762, .00103804577284, 19.94993734326,
8.739086225e-49, .01866157756892, .02275631868632, 193,
1.9723316757957, 0]).reshape(18, 9)
params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
params_table_rownames = [
'_cons', '_cons', '_cons', '_cons', '_cons', '_cons', '_cons',
'_cons', '_cons', '_cons', '_cons', '_cons', '_cons', '_cons',
'_cons', '_cons', '_cons', '_cons']
b = np.array([
1, -.50680224519119, -5.5360565201616, 0,
1, 3.0411768648574, 0, 0,
1, .00757566759698, 0, 0,
0, .00512051886486, 0, 0,
0, .02070894812762])
b_colnames = [
'_cons', '_cons', '_cons', '_cons', '_cons', '_cons', '_cons',
'_cons', '_cons', '_cons', '_cons', '_cons', '_cons', '_cons',
'_cons', '_cons', '_cons', '_cons']
b_rownames = 'y1'.split()
cov = np.array([
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, .00229579467093,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, .0586621332692, 0,
0, -.04165561908647, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
-.04165561908647, 0, 0, .08219304370043,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 1.441978380e-07,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 6.587867700e-08,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 1.077539027e-06
]).reshape(18, 18)
cov_colnames = [
'_cons', '_cons', '_cons', '_cons', '_cons', '_cons', '_cons',
'_cons', '_cons', '_cons', '_cons', '_cons', '_cons', '_cons',
'_cons', '_cons', '_cons', '_cons']
cov_rownames = [
'_cons', '_cons', '_cons', '_cons', '_cons', '_cons', '_cons',
'_cons', '_cons', '_cons', '_cons', '_cons', '_cons', '_cons',
'_cons', '_cons', '_cons', '_cons']
constraints = np.array([
1, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 1, 0,
0, 0, 1, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
1, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 1, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 1,
0, 0, 0, 0,
0, 0, 0, 1,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 1,
0, 0, 0, 0,
0, 0, 0, 0,
0, 1, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 1, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
1, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 1, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 1,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 1, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 1,
0, 0, 0, 0
]).reshape(12, 19)
constraints_colnames = [
'_cons', '_cons', '_cons', '_cons', '_cons', '_cons', '_cons',
'_cons', '_cons', '_cons', '_cons', '_cons', '_cons', '_cons',
'_cons', '_cons', '_cons', '_cons', '_r']
constraints_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12'.split()
Sigma = np.array([
.00005739073954, .00002908575565, .00022926345064, .00002908575565,
.00004096043971, .0000364524456, .00022926345064, .0000364524456,
.00158721761072]).reshape(3, 3)
Sigma_colnames = 'gdp cons inv'.split()
Sigma_rownames = 'gdp cons inv'.split()
G_var = np.array([
1, .00772119249309, .00832845872247, .00812414768988,
.00772450051084, .00839168407728, .00810118500591, .00793331513676,
.00846090823295, .009386666817, .00772119249309, .00013590038218,
.00010436399127, .00039355021854, .00008395547668, .00009296949447,
.0001468047815, .00007985818625, .00008622263703, .00012491817455,
.00832845872247, .00010436399127, .0001177915254, .0001572415776,
.00008140583018, .00008416323485, .00014479739125, .00007884622137,
.0000839417926, .00012879456896, .00812414768988, .00039355021854,
.0001572415776, .00222357091844, .0001852293649, .00023227850984,
.00042852108282, .00014155595459, .00015686829612, .00027431611677,
.00772450051084, .00008395547668, .00008140583018, .0001852293649,
.00013589031191, .00010428130248, .00039335411738, .00008365141811,
.00009289191013, .0001446903813, .00839168407728, .00009296949447,
.00008416323485, .00023227850984, .00010428130248, .000118309348,
.00015273148978, .00008252427592, .00008497769731, .00014704611828,
.00810118500591, .0001468047815, .00014479739125, .00042852108282,
.00039335411738, .00015273148978, .00222677863348, .00017054880362,
.0002272506141, .00033983014403, .00793331513676, .00007985818625,
.00007884622137, .00014155595459, .00008365141811, .00008252427592,
.00017054880362, .00013762976929, .00010632331501, .00038874930061,
.00846090823295, .00008622263703, .0000839417926, .00015686829612,
.00009289191013, .00008497769731, .0002272506141, .00010632331501,
.000119472079, .00016022586575, .009386666817, .00012491817455,
.00012879456896, .00027431611677, .0001446903813, .00014704611828,
.00033983014403, .00038874930061, .00016022586575, .00210416228523
]).reshape(10, 10)
G_var_colnames = [
'_cons',
'L.gdp', 'L.cons', 'L.inv',
'L2.gdp', 'L2.cons', 'L2.inv',
'L3.gdp', 'L3.cons', 'L3.inv']
G_var_rownames = [
'_cons',
'L.gdp', 'L.cons', 'L.inv',
'L2.gdp', 'L2.cons', 'L2.inv',
'L3.gdp', 'L3.cons', 'L3.inv']
bf_var = np.array([
-.28614799058891, .02569110476595, -.18003096181942, .6738689560015,
.29544106895159, .18370240194258, .03057777928182, -.01444291994803,
.01263245201514, .00128149319157, -.12715587337617, -.08663431448056,
-.35906668730993, .25639388994688, .20570668527827, .41845237867104,
.02404284475263, .00384555072972, .04190581088286, .00483719365525,
-1.8625374877103, .33142498594011, -.48831009148236, 4.4033743272466,
.87819807698004, -.12378698529172, .22371717935155, -.09655522236577,
.03345298758638, -.02059735685585])
bf_var_colnames = [
'L.gdp', 'L2.gdp', 'L3.gdp',
'L.cons', 'L2.cons', 'L3.cons',
'L.inv', 'L2.inv', 'L3.inv',
'_cons',
'L.gdp', 'L2.gdp', 'L3.gdp',
'L.cons', 'L2.cons', 'L3.cons',
'L.inv', 'L2.inv', 'L3.inv',
'_cons',
'L.gdp', 'L2.gdp', 'L3.gdp',
'L.cons', 'L2.cons', 'L3.cons',
'L.inv', 'L2.inv', 'L3.inv',
'_cons']
bf_var_rownames = 'r1'.split()
B = np.array([
.00757566759698, 0, 0, 0,
.00512051886486, 0, 0, 0,
.02070894812762]).reshape(3, 3)
B_colnames = 'gdp cons inv'.split()
B_rownames = 'gdp cons inv'.split()
A = np.array([
1, 0, 0, -.50680224519119,
1, 0, -5.5360565201616, 3.0411768648574,
1]).reshape(3, 3)
A_colnames = 'gdp cons inv'.split()
A_rownames = 'gdp cons inv'.split()
beq = np.array([
np.nan, 0, 0, 0,
np.nan, 0, 0, 0,
np.nan]).reshape(3, 3)
beq_colnames = 'c1 c2 c3'.split()
beq_rownames = 'r1 r2 r3'.split()
aeq = np.array([
1, 0, 0, np.nan,
1, 0, np.nan, np.nan,
1]).reshape(3, 3)
aeq_colnames = 'c1 c2 c3'.split()
aeq_rownames = 'r1 r2 r3'.split()
V_var = np.array([
.02944043907167, -.00139167936464, -.0010606099932, -.01749947940302,
.00202095994301, -.00138072504574, -.00385176007523, .00038731129816,
.00020334459451, -.00004143863419, .01492048062093, -.00070530622659,
-.00053751952583, -.00886877545113, .00102422703656, -.00069975455317,
-.00195208065406, .0001962902355, .00010305549704, -.00002100119285,
.11760811420253, -.00555945464168, -.00423690492187, -.06990659232715,
.00807329290157, -.00551569453385, -.01538693895515, .00154722391453,
.00081231717487, -.00016553827919, -.00139167936464, .03044262457881,
-.00102187006012, .00135423549927, -.01978648635158, .00141733933507,
.0005786735915, -.00404788575193, .0001576008945, -.00004691846312,
-.00070530622659, .01542839048605, -.00051788604076, .00068632959155,
-.01002783570742, .00071831075721, .0002932730754, -.00205147758736,
.00007987248718, -.00002377838245, -.00555945464168, .12161162608255,
-.0040821473633, .00540987459008, -.07904268481996, .00566196061062,
.00231167441725, -.01617041813247, .00062958109943, -.0001874289971,
-.0010606099932, -.00102187006012, .0305750957042, .00161206604309,
.00123567563375, -.01979131075828, .00006003651469, .00052765232747,
-.00406616514879, -.0000494168379, -.00053751952583, -.00051788604076,
.01549552714982, .00081699869003, .00062624318551, -.01003028072757,
.00003042664044, .00026741538424, -.00206074162672, -.0000250445644,
-.00423690492187, -.0040821473633, .12214081925136, .00643985121405,
.00493625386149, -.07906195726942, .00023983274364, .002107855628,
-.01624344032441, -.00019740945784, -.01749947940302, .00135423549927,
.00161206604309, .0174886287578, -.00252569799616, -.00071586401207,
.00214091625575, -.00039776436038, -.00032773904917, -.00001762895502,
-.00886877545113, .00068632959155, .00081699869003, .00886327631977,
-.00128002941513, -.00036280148857, .00108502116518, -.0002015878709,
-.00016609888596, -8.934393985e-06, -.06990659232715, .00540987459008,
.00643985121405, .06986324637298, -.010089611016, -.00285972013799,
.00855249213135, -.00158898161155, -.00130924581082, -.0000704238191,
.00202095994301, -.01978648635158, .00123567563375, -.00252569799616,
.02190111225253, -.00177986396497, -.00110297152268, .00248014965403,
-.00035987053166, -.00002801274167, .00102422703656, -.01002783570742,
.00062624318551, -.00128002941513, .01109953286177, -.00090203905358,
-.00055898844408, .00125694541307, -.00018238319342, -.00001419692037,
.00807329290157, -.07904268481996, .00493625386149, -.010089611016,
.08749015273475, -.00711016720733, -.00440612996585, .00990765535257,
-.00143760405483, -.00011190477537, -.00138072504574, .00141733933507,
-.01979131075828, -.00071586401207, -.00177986396497, .02191808721673,
.00005751246451, -.00100612185989, .0023961694647, -.00002263401879,
-.00069975455317, .00071831075721, -.01003028072757, -.00036280148857,
-.00090203905358, .01110813581173, .00002914744614, -.00050990481753,
.00121438406457, -.00001147097154, -.00551569453385, .00566196061062,
-.07906195726942, -.00285972013799, -.00711016720733, .08755796400357,
.00022974971529, -.0040192367482, .00957217286629, -.00009041795403,
-.00385176007523, .0005786735915, .00006003651469, .00214091625575,
-.00110297152268, .00005751246451, .0006984186117, -.00009557423262,
-4.697657444e-06, .00001087688008, -.00195208065406, .0002932730754,
.00003042664044, .00108502116518, -.00055898844408, .00002914744614,
.00035396012049, -.00004843723567, -2.380783340e-06, 5.512427248e-06,
-.01538693895515, .00231167441725, .00023983274364, .00855249213135,
-.00440612996585, .00022974971529, .00279002957959, -.00038179815311,
-.00001876611391, .0000434507567, .00038731129816, -.00404788575193,
.00052765232747, -.00039776436038, .00248014965403, -.00100612185989,
-.00009557423262, .0007270051133, -.00006681356415, .00001061820762,
.0001962902355, -.00205147758736, .00026741538424, -.0002015878709,
.00125694541307, -.00050990481753, -.00004843723567, .00036844782368,
-.00003386126432, 5.381331462e-06, .00154722391453, -.01617041813247,
.002107855628, -.00158898161155, .00990765535257, -.0040192367482,
-.00038179815311, .00290422640037, -.00026690557379, .00004241741679,
.00020334459451, .0001576008945, -.00406616514879, -.00032773904917,
-.00035987053166, .0023961694647, -4.697657444e-06, -.00006681356415,
.00069956889753, 8.959242929e-06, .00010305549704, .00007987248718,
-.00206074162672, -.00016609888596, -.00018238319342, .00121438406457,
-2.380783340e-06, -.00003386126432, .00035454308793, 4.540564432e-06,
.00081231717487, .00062958109943, -.01624344032441, -.00130924581082,
-.00143760405483, .00957217286629, -.00001876611391, -.00026690557379,
.00279462471985, .00003579021573, -.00004143863419, -.00004691846312,
-.0000494168379, -.00001762895502, -.00002801274167, -.00002263401879,
.00001087688008, .00001061820762, 8.959242929e-06, 1.677729973e-06,
-.00002100119285, -.00002377838245, -.0000250445644, -8.934393985e-06,
-.00001419692037, -.00001147097154, 5.512427248e-06, 5.381331462e-06,
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]).reshape(30, 30)
V_var_colnames = [
'L.gdp', 'L2.gdp', 'L3.gdp',
'L.cons', 'L2.cons', 'L3.cons',
'L.inv', 'L2.inv', 'L3.inv',
'_cons',
'L.gdp', 'L2.gdp', 'L3.gdp',
'L.cons', 'L2.cons', 'L3.cons',
'L.inv', 'L2.inv', 'L3.inv',
'_cons',
'L.gdp', 'L2.gdp', 'L3.gdp',
'L.cons', 'L2.cons', 'L3.cons',
'L.inv', 'L2.inv', 'L3.inv',
'_cons']
V_var_rownames = [
'L.gdp', 'L2.gdp', 'L3.gdp',
'L.cons', 'L2.cons', 'L3.cons',
'L.inv', 'L2.inv', 'L3.inv',
'_cons',
'L.gdp', 'L2.gdp', 'L3.gdp',
'L.cons', 'L2.cons', 'L3.cons',
'L.inv', 'L2.inv', 'L3.inv',
'_cons',
'L.gdp', 'L2.gdp', 'L3.gdp',
'L.cons', 'L2.cons', 'L3.cons',
'L.inv', 'L2.inv', 'L3.inv',
'_cons']
b_var = np.array([
-.28614799058891, .02569110476595, -.18003096181942, .6738689560015,
.29544106895159, .18370240194258, .03057777928182, -.01444291994803,
.01263245201514, .00128149319157, -.12715587337617, -.08663431448056,
-.35906668730993, .25639388994688, .20570668527827, .41845237867104,
.02404284475263, .00384555072972, .04190581088286, .00483719365525,
-1.8625374877103, .33142498594011, -.48831009148236, 4.4033743272466,
.87819807698004, -.12378698529172, .22371717935155, -.09655522236577,
.03345298758638, -.02059735685585])
b_var_colnames = [
'L.gdp', 'L2.gdp', 'L3.gdp',
'L.cons', 'L2.cons', 'L3.cons',
'L.inv', 'L2.inv', 'L3.inv',
'_cons',
'L.gdp', 'L2.gdp', 'L3.gdp',
'L.cons', 'L2.cons', 'L3.cons',
'L.inv', 'L2.inv', 'L3.inv',
'_cons',
'L.gdp', 'L2.gdp', 'L3.gdp',
'L.cons', 'L2.cons', 'L3.cons',
'L.inv', 'L2.inv', 'L3.inv',
'_cons']
b_var_rownames = 'y1'.split()
results_svar1_small = ParamsTableTestBunch(
params_table=params_table,
params_table_colnames=params_table_colnames,
params_table_rownames=params_table_rownames,
b=b,
b_colnames=b_colnames,
b_rownames=b_rownames,
cov=cov,
cov_colnames=cov_colnames,
cov_rownames=cov_rownames,
constraints=constraints,
constraints_colnames=constraints_colnames,
constraints_rownames=constraints_rownames,
Sigma=Sigma,
Sigma_colnames=Sigma_colnames,
Sigma_rownames=Sigma_rownames,
G_var=G_var,
G_var_colnames=G_var_colnames,
G_var_rownames=G_var_rownames,
bf_var=bf_var,
bf_var_colnames=bf_var_colnames,
bf_var_rownames=bf_var_rownames,
B=B,
B_colnames=B_colnames,
B_rownames=B_rownames,
A=A,
A_colnames=A_colnames,
A_rownames=A_rownames,
beq=beq,
beq_colnames=beq_colnames,
beq_rownames=beq_rownames,
aeq=aeq,
aeq_colnames=aeq_colnames,
aeq_rownames=aeq_rownames,
V_var=V_var,
V_var_colnames=V_var_colnames,
V_var_rownames=V_var_rownames,
b_var=b_var,
b_var_colnames=b_var_colnames,
b_var_rownames=b_var_rownames,
**est
)
results_svar1_small.__doc__ = """
Scalars
e(N) number of observations
e(N_cns) number of constraints
e(k_eq) number of equations in e(b)
e(k_dv) number of dependent variables
e(k_aux) number of auxiliary parameters
e(ll) log likelihood from svar
e(ll_#) log likelihood for equation #
e(N_gaps_var) number of gaps in the sample
e(k_var) number of coefficients in VAR
e(k_eq_var) number of equations in underlying VAR
e(k_dv_var) number of dependent variables in underlying VAR
e(df_eq_var) average number of parameters in an equation
e(df_m_var) model degrees of freedom
e(df_r_var) if small, residual degrees of freedom
e(obs_#_var) number of observations on equation #
e(k_#_var) number of coefficients in equation #
e(df_m#_var) model degrees of freedom for equation #
e(df_r#_var) residual degrees of freedom for equation # (small only)
e(r2_#_var) R-squared for equation #
e(ll_#_var) log likelihood for equation # VAR
e(chi2_#_var) chi-squared statistic for equation #
e(F_#_var) F statistic for equation # (small only)
e(rmse_#_var) root mean squared error for equation #
e(mlag_var) highest lag in VAR
e(tparms_var) number of parameters in all equations
e(aic_var) Akaike information criterion
e(hqic_var) Hannan-Quinn information criterion
e(sbic_var) Schwarz-Bayesian information criterion
e(fpe_var) final prediction error
e(ll_var) log likelihood from var
e(detsig_var) determinant of e(Sigma)
e(detsig_ml_var) determinant of Sigma_ml hat
e(tmin) first time period in the sample
e(tmax) maximum time
e(chi2_oid) overidentification test
e(oid_df) number of overidentifying restrictions
e(rank) rank of e(V)
e(ic_ml) number of iterations
e(rc_ml) return code from ml
Matrices
e(b) coefficient vector
e(Cns) constraints matrix
e(Sigma) Sigma hat matrix
e(V) variance-covariance matrix of the estimators
e(b_var) coefficient vector of underlying VAR model
e(V_var) VCE of underlying VAR model
e(bf_var) full coefficient vector with zeros in dropped lags
e(G_var) Gamma matrix saved by var; see Methods and formulas in [TS] var svar
e(aeq) aeq(matrix), if specified
e(acns) acns(matrix), if specified
e(beq) beq(matrix), if specified
e(bcns) bcns(matrix), if specified
e(lreq) lreq(matrix), if specified
e(lrcns) lrcns(matrix), if specified
e(Cns_var) constraint matrix from var, if varconstraints() is specified
e(A) estimated A matrix, if a short-run model
e(B) estimated B matrix
e(C) estimated C matrix, if a long-run model
e(A1) estimated A bar matrix, if a long-run model
""" # noqa:E501