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Re: st: Count data regression


From   Scott Cunningham <scunning@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Count data regression
Date   Thu, 23 Mar 2006 11:42:35 -0500

You can run a Poisson if you correct the standard errors for overdispersion. This can be done using bootstrapping or by using the -, robust- option.
On Mar 23, 2006, at 10:11 AM, Hugh Colaco wrote:


My dependent variable is the number of days, a count variable which is
censored from below at 2. The summary stats are below (see # 1). As
you can see, the number of days ranges from 2 to 2426. Since the
unconditional variance > mean, I assume that I should use a Neg
binomial reg rather than a Poisson reg.

However, if I take the log of the number of days, then the
unconditional variance < mean (see # 2), so I could run a Poisson reg.

Any thoughts on the above? Is there any issue if I first take the log
and then run a poisson or neg binomial regression? Would it defeat the
very purpose of the count regression? Anything else I need to
consider?


Thanks,

Hugh


1)

Days
-------------------------------------------------------------
      Percentiles      Smallest
 1%            2              2
 5%            2              2
10%            4              2       Obs                3728
25%            7              2       Sum of Wgt.        3728
50%           17                      Mean           47.29855
                        Largest       Std. Dev.      104.9396
75%           45           1268
90%          114           1607       Variance       11012.31
95%          187           1781       Skewness        8.80124
99%          448           2426       Kurtosis       131.2864



2)
 lnDays
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .6931472       .6931472
 5%     .6931472       .6931472
10%     1.386294       .6931472       Obs                3728
25%      1.94591       .6931472       Sum of Wgt.        3728

50%     2.833213                      Mean           2.927268
                        Largest       Std. Dev.      1.298882
75%     3.806663       7.145196
90%     4.736198       7.382124       Variance       1.687095
95%     5.231109       7.484931       Skewness       .3821456
99%     6.104793       7.793999       Kurtosis       2.691119

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