Dear All,
I wrote a small ado.file ("mcar_ordinal_6"), given below, to get bootstrap
std. err. estimates for the coefficients of a binary logit model with one
categorical variable (given by the dummies x1 - x5) and one continuous
variable Z4. I compare these estimates (obtained by typing: . bootstrap
"mcar_ordinal_6" ....) with the usual bootstrapped logit std. err's
(obtained by typing: . bootstrap "logit Y x1 x2 x3 x4 x5 Z4" _b, reps(1000)
dots). To my surprise, both ways result in totally different std. err.
estimates (given below)!
What is wrong with my ado.file? Any suggestions?
Thank's a lot
Thomas
program define mcar_ordinal_6, rclass
version 8.2
logit Y x1 x2 x3 x4 x5 Z4
mat beta=e(b)
return scalar x1=beta[1,1]
return scalar x2=beta[1,2]
return scalar x3=beta[1,3]
return scalar x4=beta[1,4]
return scalar x5=beta[1,5]
return scalar Z4=beta[1,6]
return scalar const=beta[1,7]
end
. bootstrap "mcar_ordinal_6" const=r(const) Z4=r(Z4) x1=r(x1) x2=r(x2)
x3=r(x3) x4=r(x4) x
> 5=r(x5) , reps(1000) dots
Bootstrap statistics Number of obs =
12451
Replications =
1000
----------------------------------------------------------------------------
--
Variable | Reps Observed Bias Std. Err. [95% Conf. Interval]
-------------+--------------------------------------------------------------
--
const | 559 5.9239 .2659001 3.056453 -.0796589 11.92746
(N)
| .3723237 11.91934
(P)
| .167534 11.36338
(BC)
Z4 | 973 -.1012986 2.435581 3.419307 -6.811373 6.608775
(N)
| -.1615792 10.09536
(P)
| -2.702652 6.346565
(BC)
x1 | 1000 -3.159859 .0387406 .7028649 -4.53912 -1.780598
(N)
| -4.705648 -1.790577
(P)
| -4.807204 -1.910804
(BC)
x2 | 1000 -3.567799 .221243 .762015 -5.063133 -2.072466
(N)
| -4.854494 -1.469348
(P)
| -5.062595 -2.441963
(BC)
x3 | 1000 -3.020464 .755457 1.099255 -5.177578 -.8633499
(N)
| -4.334947 -.2468368
(P)
| -4.853861 -1.190173
(BC)
x4 | 1000 -1.279128 .4245199 .660478 -2.575211 .0169558
(N)
| -1.876799 .2625169
(P)
| -2.165759 -.2838531
(BC)
x5 | 999 -.365843 .2844974 1.212824 -2.74582 2.014134
(N)
| -.9417797 1.215659
(P)
| -1.233725 -.0630795
(BC)
----------------------------------------------------------------------------
--
. bootstrap "logit Y x1 x2 x3 x4 x5 Z4" _b, reps(1000) dots
Bootstrap statistics Number of obs =
12451
Replications =
1000
----------------------------------------------------------------------------
--
Variable | Reps Observed Bias Std. Err. [95% Conf. Interval]
-------------+--------------------------------------------------------------
--
b_x1 | 618 -3.159859 .4253625 .5268016 -4.1944 -2.125317
(N)
| -3.579062 -1.61639
(P)
| -3.82373 -2.401615
(BC)
b_x2 | 939 -3.567799 -.040688 .6170578 -4.778773 -2.356826
(N)
| -4.897816 -2.479752
(P)
| -4.931322 -2.527777
(BC)
b_x3 | 985 -3.020464 -.0677732 .6212142 -4.239521 -1.801407
(N)
| -4.528006 -1.958316
(P)
| -4.468464 -1.938175
(BC)
b_x4 | 1000 -1.279128 .0311197 .3723199 -2.009746 -.5485089
(N)
| -1.958411 -.4930105
(P)
| -1.960265 -.4994082
(BC)
b_x5 | 1000 -.365843 .0290434 .3521309 -1.056844 .3251582
(N)
| -.9971553 .4314241
(P)
| -1.042248 .3425232
(BC)
b_Z4 | 1000 -.1012986 -.0010356 .0334018 -.1668444 -.0357529
(N)
| -.1695864 -.0428685
(P)
| -.1746111 -.0461127
(BC)
b_cons | 1000 5.9239 .0343191 3.088323 -.1364438 11.98424
(N)
| .5156874 12.15566
(P)
| .706053 12.60186
(BC)
----------------------------------------------------------------------------
--
Note: N = normal
P = percentile
BC = bias-corrected
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