Thomas,
In the -bootstrap- statements you require 1000 replications. However, when
looking at the output, in a lot of cases Stata reports less than 1000
replications for some variables. I think that in some replications you
simply had "bad luck". The bootstrap samples were such that in these
replications you had obtained explanatory variables that perfectly
predicted the dependant variable. Using Stata's -logit- command in such a
case will result in error messages of the following type:
"note: x2 != 0 predicts failure perfectly
x2 dropped and 36 obs not used"
Thus if for instance the variable x2 is dropped, its coefficient cannot be
posted by the -bootstrap- procedure. I guess that a missing value is
posted in this case. Now, this does not yet explain the different results
in your case. However, looking at your .ado file (mcar_ordinal_6), it
becomes clear. If the coefficient on the x2 variable is missing, Stata
will return the coefficient of x3 in r(x2), x4 in r(x3) ... and finally a
missing value for the constant (the last scalar to be returned).
By the way, so as ensure identical results, you should set the random
number seed (-help seed-) to an identical value before each call to
-bootstrap-.
Hope this helps,
Jean
>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)
>----------------------------------------------------------------------------
>--
>
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