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# Re: st: Binary Choice Model and fixed effects - interpreting the interaction effects?

 From Benjamin Niug To statalist@hsphsun2.harvard.edu Subject Re: st: Binary Choice Model and fixed effects - interpreting the interaction effects? Date Mon, 2 Apr 2012 16:18:30 +0200

```@Maarten. Thanks again.

Am 2. April 2012 16:00 schrieb Maarten Buis <maartenlbuis@gmail.com>:
> That is due to the effect of that the constant is not calculated in
> these models. So you'll have to interpret the odds ratios without the
> baseline odds. This is not ideal but can be done, and I guess that is
> the price you'll have to pay for estimating a fixed effects model...
>
> -- Maarten
>
> On Mon, Apr 2, 2012 at 3:39 PM, Benjamin Niug wrote:
>> Maarten - thanks a lot for clarification.
>>
>> In case of a clogit xtlogit, the baseline-trick you applied, namely
>> generating a variable
>>
>> gen baseline = 1
>>
>> and then running the logit-regression also on baseline such that the
>> odds ratios of the interaction effect can be compared to the baseline
>> odds ratio does not work due to multicollinearity of the baseline
>> variable.
>>
>> How do I solve this problem? I guess a constant could serve the same
>> purpose as your baseline variable - however it is not reported
>> neither. How do I still come up with a meaningful interpretation?
>>
>>
>>
>> Am 2. April 2012 15:17 schrieb Maarten Buis <maartenlbuis@gmail.com>:
>>> On Mon, Apr 2, 2012 at 2:37 PM, Benjamin Niug  wrote:
>>>> @Maarten. Thanks. I tried to calculated the marginal effects as
>>>> indicated in the paper you mentioned (M.L. Buis (2010) "Stata tip 87:
>>>> Interpretation of interactions in non-linear models", The Stata
>>>> Journal, 10(2), pp. 305-308)
>>>>
>>>> However, some interactions are not estimated / "estimable" by Stata
>>>> using the -margins- command.
>>>
>>> The point of that article is that you should _not_ estimate marginal
>>> effects. In that article I tried to be nice towards Edward Norton and
>>> colleagues and tried to find some situation where marginal effects
>>> might make some sense. I did find such a special situation in the case
>>> of a fully saturated model(*), but in practice you should just forget
>>> about that and go for odds ratios. In retrospect that inclusion of
>>> marginal effects in the article was a mistake as this confuses more
>>> than it helps.
>>>
>>> So the bottom line is: There is only one solution and that is to
>>> interpret the results in terms of odds ratios.
>>>
>>> Hope this helps,
>>> Maarten
>>>
>>> (*) A fixed effects model with covariates cannot be a fully saturated
>>> model, so this is not an "escape route" open to you. You really really
>>> really have no other option than to learn how to use and report odds,
>>> odds ratios and ratios of odds ratios.
>>>
>>>
>>>
>>> --------------------------
>>> Maarten L. Buis
>>> Institut fuer Soziologie
>>> Universitaet Tuebingen
>>> Wilhelmstrasse 36
>>> 72074 Tuebingen
>>> Germany
>>>
>>>
>>> http://www.maartenbuis.nl
>>> --------------------------
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>>
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>
>
>
> --
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
>
> http://www.maartenbuis.nl
> --------------------------
>
> *
> *   For searches and help try:
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```