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RE: Re: st: GLLAMM multinomial: tremendous instability


From   jverkuilen <jverkuilen@gc.cuny.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: Re: st: GLLAMM multinomial: tremendous instability
Date   Thu, 4 Dec 2008 11:46:16 -0500

I am not sure I have any citations that would be persuasive for an econometric audience, but you might look thru Kenneth Train's book (Discrete Choice Methods with Simulation, Cambridge, 2003). Random variation is not guaranteed---in a sense if it *doesn't* happen that's good because it says the world is simpler. 


-----Original Message-----
From: "KONSTANTARAS KONSTANTINOS" <dinokon@otenet.gr>
To: statalist@hsphsun2.harvard.edu
Sent: 12/4/2008 11:21 AM
Subject: RE: Re: st: GLLAMM multinomial: tremendous instability

Thanks a lot for your advice.

It seems that I do fall into the category you have described of a very small random effect for one category, because estimating the model with only this one random effect does not produce LR significant results for it or, in some other specifications, estimation breaks down completely. 

The other random effect does not have this problem and is significant from an LR test standpoint.

I believe I can live with one RE not existent, but I would like to ask you once again if there is any reference on the matter in order to figure out whether I can indeed draw any safe conclusions from this model, assuming only one significant random effect. The theoretical explanation for this might be that individuals choosing to belong in the non-existent random effects category do not have a sufficiently dense -deep- panel time dimension, hence failing to generate sufficient depth of time-independent observations to estimate their unobserved heterogeneity through time (they behave more like a single cross section and not a panel).  

Thanking you in advance,

Dino K.
................................................................
Verkuilen, Jay has sent on: Wednesday, December 03, 2008 9:36 PM 
>Alternatively, you could have a random effect term that really "wants" 
>to
be 0 and thus has a profile likelihood piled up on 0. This can often cause odd things to happen in the random effects covariance matrix if it's been constrained to be positive semi-definite. Estimation breaks down in the presence of really small random effects. 

Start deleting random effects and see if it goes away. An alternative (and maybe better) strategy would be to start from a model you know is identified, e.g., the multinomial choice model with no random effects, and add RE terms in an order determined by theory.
-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
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