Hi Klaus,
thanks, that's actually excatly what I did. Sorry for not being clear
enough in my response to Maarten.
Best,
Nils
On Tue, Jan 5, 2010 at 5:20 PM, Klaus Pforr <[email protected]> wrote:
> Hi Nils,
>
> instead of switching to a 2year interval and loose efficiency you can
> reduce the number of year dummies. You probably add the dummies only to
> control for unknown period effects which you still could capture by
> adding only a dummy for 2-year interval while maintaining the yearly
> variance for the other variables.
>
> Best
>
> Klaus
>
> Nils Braakmann schrieb:
>> Hi Martin,
>>
>> thanks for your input. I have actually traced the problem to the (low)
>> number of individuals for whom the outcome and the variable of
>> interest switched simultaneously (see my previous mail) in each year.
>> Simple idiosyncracies, inconsistencies and stuff like that were my
>> first idea, but the fact that I had the problem with various datasets
>> in the context of different questions made this somewhat unlikely.
>>
>> Best,
>> Nils
>>
>> On Tue, Jan 5, 2010 at 3:42 PM, Martin Weiss <[email protected]> wrote:
>>> <>
>>>
>>> Are you sure you are familiar with all idiosyncracies of your data? Use
>>> -codebook- and -inspect- to check. Sometimes convergence problems can be
>>> traced back to issues with the data which the two commands make plain within
>>> seconds...
>>>
>>>
>>>
>>> HTH
>>> Martin
>>>
>>> -----Ursprüngliche Nachricht-----
>>> Von: [email protected]
>>> [mailto:[email protected]] Im Auftrag von Nils Braakmann
>>> Gesendet: Dienstag, 5. Januar 2010 15:00
>>> An: [email protected]
>>> Betreff: Re: st: Some problems with clogit convergence when adding year
>>> dummies
>>>
>>> Hi Maarten,
>>>
>>> thanks for your input, but I'm not sure that this is the problem. The
>>> year effects are identical for everyone, while my variables of
>>> interest (assume a dummy for the sake of simplicity) switch in
>>> different years for different people. In other words, the model is
>>>
>>> y_it = 1{a_i + g_t + tau*d_it + u_it > 0}, where a_i is the individual
>>> fixed effect, g_t is the common time effect and d_it is the variable
>>> of interest. What you have in mind sound to me like a model of the
>>> sort
>>> y_it = 1{a_i* g_t + tau*d_it + u_it > 0}, that is a model with
>>> individual specific time effects which would indeed exhaust all
>>> available information.
>>>
>>>
>>> What could be the case though is that I end with too few individuals
>>> for whom the dependent variable and the variable of interest change
>>> jointy in each year. In fact, I just tried to use two-year intervals
>>> annd now everything runs fine and looks reasonable (at least at a
>>> first glance).
>>>
>>> Thanks again and best wishes,
>>> Nils
>>>
>>>
>>> On Tue, Jan 5, 2010 at 2:35 PM, Maarten buis <[email protected]>
>>> wrote:
>>>> --- On Tue, 5/1/10, Nils Braakmann wrote:
>>>>> I have a large panel of roughly 50,000 observations over 20
>>>>> years. I model the effects of a time-varying variable on individual
>>>>> employment probabilities using -clogit- with the individuals as
>>>>> groups. The trouble starts when I add year dummies, which leads to
>>>>> severe convergence problems (nonconcave likelihood for several
>>>>> hundred iterations, etc.).
>>>> So you have data on individuals observed for multiple years.
>>>> With a fixed effects model you have taken out all the information
>>>> you might obtain from comparing individuals. This is a good thing
>>>> in the sense that by only comparing individuals with themselves
>>>> you are more likely to compare like with like, but it is also a
>>>> bad thing as you are throwing away information. If your data is
>>>> collected annually, and you added year dummies, then it seems to
>>>> me that the fixed effects in combination with the year dummies
>>>> will have exhausted all the information that is present in your
>>>> data, and that the effect of any additional variables are thus not
>>> identified, and Stata will show that by not converging.
>>>> Hope this helps,
>>>> Maarten
>>>>
>>>> --------------------------
>>>> 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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>>
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>
> --
> __________________________________
>
> Klaus Pforr
> MZES AB - A
> Universität Mannheim
> D - 68131 Mannheim
> Tel: +49-621-181 2801
> fax: +49-621-181 2803
> URL: http://www.mzes.uni-mannheim.de
>
> Besucheranschrift: A5, Raum A312
> __________________________________
>
>
>
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