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st: failure estimating bootstrap on logistic model
From
Andrew Waxman <[email protected]>
To
[email protected]
Subject
st: failure estimating bootstrap on logistic model
Date
Wed, 3 Mar 2010 20:07:24 -0500
Hi,
I am trying to estimate a logistic model to predict firm exit and want to
bootstrap my standard errors. My dataset is large (225,000 obs) and
although there are some missing values, this shouldn't significantly
restrict the number of observations when bootstrap draws a sample.
However, the majority of reps seem to fail (note the "x"'es below) when I
estimate using a logistic model:
*. bootstrap, r(50): logistic exit lnfirmage lnL lnL2 skratio fempsh fown
gown exporter lnrKreppw lnTFPCDols yd2*
*> -yd11 provd2-provd26 if year!=1990 & tc==2 & outlier!=1*
*(running logistic on estimation sample)*
*
*
*Bootstrap replications (50)*
*----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 *
*xxxxxx.xxxx.xxxxxxxx.xxxxxxxxxx.x.xxxxxxxxx.xx.xxx 50*
*
*
*note: yd2 dropped because of collinearity*
*note: yd3 dropped because of collinearity*
*note: yd4 dropped because of collinearity*
*note: yd5 dropped because of collinearity*
*note: yd6 dropped because of collinearity*
*note: yd7 dropped because of collinearity*
*note: yd8 dropped because of collinearity*
*note: yd11 dropped because of collinearity*
*
*
*Logistic regression Number of obs =
25380*
* Replications =
7*
* Wald chi2(6) =
.*
* Prob > chi2 =
.*
*Log likelihood = -5468.5047 Pseudo R2 =
0.1066*
*
*
*
------------------------------------------------------------------------------
*
* | Observed Bootstrap Normal-based*
* exit | Odds Ratio Std. Err. z P>|z| [95% Conf.
Interval]*
*
-------------+----------------------------------------------------------------
*
* lnfirmage | .8826269 .0127857 -8.62 0.000 .8579197
.9080457*
* lnL | .0688836 .0104223 -17.68 0.000 .0512068
.0926627*
* lnL2 | 1.23799 .0172181 15.35 0.000 1.204699
1.272201*
* skratio | 1.914752 .3221713 3.86 0.000 1.376865
2.662769*
* fempsh | 1.465115 .2135063 2.62 0.009 1.101105
1.94946*
* fown | 1.246216 .1670346 1.64 0.101 .9583057
1.620626*
* gown | .7605959 .0838717 -2.48 0.013 .6127609
.9440975*
* exporter | 1.568062 .1541614 4.58 0.000 1.293239
1.901288*
* lnrKreppw | .9256719 .0146664 -4.87 0.000 .8973681
.9548684*
*lnTFPCDolsss | .9377086 .038908 -1.55 0.121 .8644688
1.017153*
* yd9 | .6756658 .0590381 -4.49 0.000 .5693193
.8018773*
* yd10 | 2.429092 .1524321 14.14 0.000 2.147973
2.747003*
* provd2 | 1.357248 11.32525 0.04 0.971 1.07e-07
1.72e+07*
* provd3 | 1.018087 8.62425 0.00 0.998 6.27e-08
1.65e+07*
* provd4 | .4307106 3.530132 -0.10 0.918 4.55e-08
4080270*
* provd5 | 3.372531 26.91701 0.15 0.879 5.42e-07
2.10e+07*
* provd6 | 1.235142 10.24105 0.03 0.980 1.08e-07
1.41e+07*
* provd7 | 5.591253 48.60804 0.20 0.843 2.23e-07
1.40e+08*
* provd8 | 4.408462 37.58628 0.17 0.862 2.44e-07
7.97e+07*
* provd9 | 1.281082 10.76104 0.03 0.976 9.07e-08
1.81e+07*
* provd10 | 1.255 10.46518 0.03 0.978 1.00e-07
1.57e+07*
* provd11 | 1.54895 12.95652 0.05 0.958 1.17e-07
2.04e+07*
* provd12 | 1.453425 12.24842 0.04 0.965 9.75e-08
2.17e+07*
* provd13 | 1.401437 11.68034 0.04 0.968 1.13e-07
1.74e+07*
* provd14 | 2.839406 23.93431 0.12 0.901 1.90e-07
4.25e+07*
* provd15 | 1.465131 12.07134 0.05 0.963 1.42e-07
1.51e+07*
* provd16 | 15.04903 128.1326 0.32 0.750 8.51e-07
2.66e+08*
* provd17 | 1.609744 13.47251 0.06 0.955 1.21e-07
2.14e+07*
* provd18 | 1.460923 12.50193 0.04 0.965 7.59e-08
2.81e+07*
* provd19 | 1.959788 16.44873 0.08 0.936 1.41e-07
2.73e+07*
* provd20 | 1.334879 11.01907 0.03 0.972 1.26e-07
1.42e+07*
* provd21 | 2.486556 20.49772 0.11 0.912 2.39e-07
2.58e+07*
* provd22 | 5.840088 48.96986 0.21 0.833 4.26e-07
8.01e+07*
* provd23 | 1.392887 11.71483 0.04 0.969 9.66e-08
2.01e+07*
* provd24 | 6.23053 51.59921 0.22 0.825 5.56e-07
6.98e+07*
* provd25 | 1.453785 11.93851 0.05 0.964 1.49e-07
1.42e+07*
* provd26 | 2.422081 21.33134 0.10 0.920 7.72e-08
7.60e+07*
*
------------------------------------------------------------------------------
*
*Note: one or more parameters could not be estimated in 43 bootstrap
replicates;*
* standard error estimates include only complete replications.*
Yet when I perform the same procedure using OLS, none of the reps fail:
*
*
*. bootstrap, r(50): reg exit lnfirmage lnL lnL2 skratio fempsh fown gown
exporter lnrKreppw lnTFPCDols yd2-yd11*
*> provd2-provd26 if year!=1990 & tc==2 & outlier!=1*
*(running regress on estimation sample)*
*
*
*Bootstrap replications (50)*
*----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 *
*.................................................. 50*
*
*
*Linear regression Number of obs =
25380*
* Replications =
50*
* Wald chi2(38) =
.*
* Prob > chi2 =
.*
* R-squared =
0.0503*
* Adj R-squared =
0.0489*
* Root MSE =
0.2409*
*
*
*
------------------------------------------------------------------------------
*
* | Observed Bootstrap Normal-based*
* exit | Coef. Std. Err. z P>|z| [95% Conf.
Interval]*
*
-------------+----------------------------------------------------------------
*
* lnfirmage | -.0064203 .0019687 -3.26 0.001 -.0102788
-.0025617*
* lnL | -.1464816 .008121 -18.04 0.000 -.1623984
-.1305648*
* lnL2 | .0122006 .0008017 15.22 0.000 .0106292
.013772*
* skratio | .0374925 .0098978 3.79 0.000 .0180931
.0568919*
* fempsh | .0197131 .0068764 2.87 0.004 .0062356
.0331907*
* fown | .0089389 .0055844 1.60 0.109 -.0020063
.0198841*
* gown | -.0090385 .0070631 -1.28 0.201 -.0228819
.0048049*
* exporter | .0202212 .0038746 5.22 0.000 .0126271
.0278153*
* lnrKreppw | -.0036477 .0007043 -5.18 0.000 -.0050281
-.0022673*
*lnTFPCDolsss | -.0040976 .0022188 -1.85 0.065 -.0084463
.0002511*
* yd2 | (dropped)*
* yd3 | (dropped)*
* yd4 | (dropped)*
* yd5 | (dropped)*
* yd6 | (dropped)*
* yd7 | (dropped)*
* yd8 | 0 .0284675 0.00 1.000 -.0557954
.0557954*
* yd9 | -.0170108 .0285136 -0.60 0.551 -.0728965
.0388749*
* yd10 | .0576383 .0282818 2.04 0.042 .002207
.1130696*
* yd11 | (dropped)*
* provd2 | .0118879 .0283366 0.42 0.675 -.0436508
.0674265*
* provd3 | -.0022906 .032666 -0.07 0.944 -.0663148
.0617335*
* provd4 | -.0153793 .0308718 -0.50 0.618 -.075887
.0451285*
* provd5 | .077172 .0380442 2.03 0.043 .0026067
.1517373*
* provd6 | .0077553 .03216 0.24 0.809 -.0552772
.0707877*
* provd7 | .1416116 .0861636 1.64 0.100 -.027266
.3104892*
* provd8 | .1190133 .0383966 3.10 0.002 .0437574
.1942692*
* provd9 | .0116131 .0288091 0.40 0.687 -.0448516
.0680779*
* provd10 | .0104755 .0273441 0.38 0.702 -.043118
.0640689*
* provd11 | .0246304 .0285513 0.86 0.388 -.0313292
.08059*
* provd12 | .0191271 .0283466 0.67 0.500 -.0364312
.0746854*
* provd13 | .0158017 .0274104 0.58 0.564 -.0379216
.069525*
* provd14 | .0780028 .0331598 2.35 0.019 .0130108
.1429948*
* provd15 | .0197735 .0307751 0.64 0.521 -.0405445
.0800916*
* provd16 | .3027415 .0882585 3.43 0.001 .1297581
.4757249*
* provd17 | .020612 .0284689 0.72 0.469 -.035186
.0764099*
* provd18 | .0163567 .0359175 0.46 0.649 -.0540403
.0867537*
* provd19 | .0308439 .0398664 0.77 0.439 -.0472929
.1089807*
* provd20 | .0144719 .0313564 0.46 0.644 -.0469856
.0759294*
* provd21 | .0602479 .0296055 2.04 0.042 .0022222
.1182736*
* provd22 | .1165868 .0584628 1.99 0.046 .0020018
.2311717*
* provd23 | .0159528 .0275981 0.58 0.563 -.0381384
.070044*
* provd24 | .1591771 .0483605 3.29 0.001 .0643922
.2539621*
* provd25 | .0154854 .0416812 0.37 0.710 -.0662083
.0971791*
* provd26 | .0420502 .0425296 0.99 0.323 -.0413064
.1254067*
* _cons | .4176809 .0461915 9.04 0.000 .3271473
.5082146*
*
------------------------------------------------------------------------------
*
Is this problem related to the non-linearity of the estimation method? Is
there an obvious fix?
Very grateful for your help.
Andrew Waxman
World Bank, Research Dept.
*
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