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st: Blown up Std. errors in logistic regression with bootstrap


From   Michael Wahman <Michael.Wahman@svet.lu.se>
To   statalist@hsphsun2.harvard.edu
Subject   st: Blown up Std. errors in logistic regression with bootstrap
Date   Mon, 13 Dec 2010 13:24:17 +0100

Dear Statalist subscribers,


I have run in to a major problem when trying to run a robustness check on one of my logistic regression models, using bootstrapped robust standard errors.

I am doing a study with two different logistic models, where n is fairly small. In one of the models n is somewhat bigger (n=107) and one model has a smalle r n (n=51). I want to use robust bootstrapped standard errors to compensate for the small n, especially in the second model. I've understood that it is problematic to use MLE when the number of d.f. s is small, since this model might not be asymptotic.

I have experimented with bootstraps, but the standard errors in the model become huge. This seems to be associated with the models with a small number of df.s. If I run the models with a higher n with a bootstrap, I don’t get this problem. Neither do I get it when excluding the control variables. I have also tried to use the jackknife command. This is marginally better, but still all the variables become insignificant.

Below you can see the main model



. bootstrap: logit  oppcoaltype124 stunipolardistance lgdpgro
ldeltaifhpol lifhpol llndistmag orpreselec parelection dif12party
(running logit on estimation sample)

Bootstrap replications (50)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
.............x....................................    50

Logistic regression                             Number of obs
=        51
                                                Replications
=        49
                                                Wald chi2(8)
=      0.00
                                                Prob > chi2
=    1.0000
Log likelihood = -10.060672                     Pseudo R2
=    0.6217

------------------------------------------------------------------------------
             |   Observed   Bootstrap                         Normal-
based
oppcoalt~124 |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------
+----------------------------------------------------------------
stunipolar~e |   7.811848   5599.462     0.00   0.999    -10966.93
10982.56
     lgdpgro |   -.883474   678.2268    -0.00   0.999    -1330.183
1328.417
ldeltaifhpol |   4.764161   4410.229     0.00   0.999    -8639.125
8648.654
     lifhpol |   .8676982   764.7985     0.00   0.999     -1498.11
1499.845
  llndistmag |   .1585821   325.1274     0.00   1.000    -637.0795
637.3966
  orpreselec |  -1.598148   915.6822    -0.00   0.999    -1796.302
1793.106
 parelection |  -2.872295   2864.866    -0.00   0.999    -5617.907
5612.162
  dif12party |   .0150251   51.14297     0.00   1.000    -100.2234
100.2534
       _cons |   -8.61135   6590.954    -0.00   0.999    -12926.64
12909.42
------------------------------------------------------------------------------
Note: one or more parameters could not be estimated in 1 bootstrap
replicate;
      standard-error estimates include only complete replications.



Another problem is that I do not succede to use the cluster option. I have tried two different commands



1) bootstrap, cluster(siffra):logit oppcoaltype124 stunipolardistance lgdpgro ldeltaifhpol lifhpol llndistmag orpreselec parelection dif12party

Receives the answer:

repeated time values within panel
the most likely cause for this error is misspecifying the cluster(),
idcluster(), or group() option

I am sure I do not have repeated time values. If I run the tsset command, there is no problem. There might be some misspecification of the command, but I don't understand what that might be.



2) logit oppcoaltype124 stunipolardistance lgdpgro ldeltaifhpol lifhpol

llndistmag orpreselec parelection dif12party, vce(boot) cluster(country)

Receives the answer:

no observations


Do anyone get what the problem might be?


I would just be enormously thankful if anyone could help me with this. I suspect that something is wrong here given that the standard errors increase so drastically.


Thank you for your help,

Michael
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