Re: st: xtlogit postestimation with predict

[Steve Samuels <sjsamuels@gmail.com>]

Dave Ohls <daveohls@gmail.com>:

Two misunderstandings here:

1. -xtlogit, fe- is  conditional logistic regression, -clogit- in
Stata- (Manual). Conditional logistic regression does not
estimate the group specific intercepts. Without those intercepts, you
cannot estimate the "real" (i.e. unconditional) probabilities of events.

2. The default "pc1"  prediction after -xtlogit- is, according to the -help-,
the "probability of a positive outcome conditional on one positive
outcome within group." Thus it is a characteristic of the observed
group, not just of the individual covariate values. If you subset the
-predict- command, the composition of some groups may  change. If
a group is left with no positive outcomes, subsequent predictions  for
that group will be missing. So, there is no reason after

. predict p1
. predict p2 if dummy==1

to expect that p1 and p2 will be identical.


Steve
sjsamuels@gmail.com

On Jul 9, 2013, at 1:34 PM, Richard Herron wrote:

I don't really understand the question, but I will offer that panel
logit with fixed effects is not the same as a logit model with
indicator variables.

To estimate the model there must be within individual variation in the
dependent variable, so -xtlogit, fe- drops any individuals that don't
change state.

Do you have many individuals that don't change state?

On Tue, Jul 9, 2013 at 3:27 PM, Dave Ohls <daveohls@gmail.com> wrote:
> I am getting inconsistent sets of results using the -predict- command
> for postestimation predicted probabilities after -xtlogit- models.
> I'm using Stata/IC 11.2 for Windows.
> 
> I am estimating fixed effects logit models using code of the form:
> -xtlogit DV IV1 IV2 CV1 CV2 if CV3==1, fe-
> and want to interpret substantive results on continuous IV1 in terms
> of predicted probabilities at different values.  Because effects are
> non-linear and dependent on values of the FE/other vars, I'm
> considering these within specific substantively-important cases.
> 
> To do so, I create 5-10 dummy copies (labeled with a 1 in a variable
> called dummy) of a particular case and delete the dependent variable
> so as not to include it in the estimation of the model itself.  I keep
> all variable values as they are in the real case, except altering IV1
> to set it at its minimum for one of the copies, mean in another, max
> in another, mean plus 1 SD in another, etc.  I then estimate the
> model, followed by postestimation commands.
> 
> The problem is that I get very different sets of results when I run:
> -predict p1 if dummy==1-
> than when I run:
> -predict p2-
> The numbers aren't the same even within those cases (dummy==1) where I
> get a predicted probability in each.  I assume this is something to do
> with how it handles the fixed effects, but I can't tell from the
> manual/past forum topics/etc what it is, or which is correct.
> 
> Also, I get a totally different (third) set of results when I run:
> -predict p3, pu0-
> Given info in the manual I interpret this set as the predicted
> probabilities when the FEs are set to 0, which is not substantively
> correct for what I'm trying to do - I include it here only to show
> that that's not what's happening in either set of results above.
> 
> I have tried replicating this on other datasets and can't get the same
> inconsistency.  Any ideas?
> 
> Thanks so much for your time.
> 
> -Dave
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