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Re: st: gllamm or xtmixed models?


From   Alfonso Sánchez-Peñalver <[email protected]>
To   Stata List <[email protected]>
Subject   Re: st: gllamm or xtmixed models?
Date   Fri, 31 Jan 2014 11:23:38 -0500

Antonio,

before you do the fixed effects estimation you have to run -xtset- as

xtset country id

to follow the example you gave. Country is the grouping variable, and id is the individual observation variable. You have changed the example in the last email with respect to the previous ones. Going back to the previous ones you can run the fixed effects estimation as

xtreg depression x1 x2 x3, fe.

Now, in your last email you use -vce(cluster country)-. Using cluster robust variance accounts for correlation of the errors within the clusters, in your case the countries, but not across clusters. The question is whether once you have stripped the errors of the different intercepts by using fixed effects, why do you expect the errors to be correlated within the countries? Consider for example that there is an unobservable variable which measures severity of winter. We expect that the more severe the winter is the more cases of depression, so while controlling for everything else it would make sense that the number of patients with depression in Sweden or Norway is larger than the number of patients with depression in Spain or Italy. Since we cannot control for the severity of the winter because we don’t have a measure for it, the errors would capture the effect of this variable on the dependent variable, and thus you would expect the errors for Sweden to be larger than the!
  errors for Spain, which creates the correlation between the errors in Sweden, and the correlation between the errors in Spain, but not the correlation between errors of Spain and Sweden. Now, when you use fixed effects estimation you are in fact controlling for the average effect of all unobserved characteristics of the countries, so you would be controlling for the average effect of the severity of the winter (among other unobservables) in the countries. Therefore, unless you think there is something else causing the correlation between the errors within the different countries, you don’t need the -vce(cluster country)- option.

Best,

Alfonso Sánchez-Peñalver, PhD

Visiting Assistant Professor
Suffolk University
Senior Instructor
UMass Boston



On Jan 31, 2014, at 4:26 AM, Antonio Rodriguez Andres <[email protected]> wrote:

> Alfonso
> 
> Thank you for your answer. As far as I understood, as the observations are
> clustered within countries. I have to account this in my model and use a two
> multilevel model. What I can try is a fixed effects model with clustering at
> country level
> 
> xtreg dv iv, fe vce (cluster country)
> 
> I should also use the xtset command but I do not have a real panel. Usually
> we declare with xtset id year (both dimensions of the panel data ) but here
> it is only a cross section
> 
> Can I type
> 
> xtset id  country  (1 level and second level)?
> 
> 
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of Alfonso
> Sánchez-Peñalver
> Sent: Thursday, January 30, 2014 10:31 PM
> To: Stata List
> Subject: Re: st: gllamm or xtmixed models?
> 
> Hi again Antonio,
> 
> I haven't used -gllamm- (SSC) but my understanding is that you will also be
> able to estimate the random effects with it. The fixed effects can be
> estimated in two different ways:
> 
> 1. Pooled OLS (-regress-) with a dummy variable for each country and no
> constant (-nocons- option) 2. -xtreg- with fe option
> 
> For the second option you will have to first use -xtset- to identify which
> is the level 2 (cluster) variable (country) and the level 1 variable (the
> individuals).
> 
> As for random slopes, consider the random effects model. The random effects
> model assumes that the intercept is a random variable across countries. What
> if the intercept is not the only thing that varies across countries? What if
> the effect (slope) of a certain variable (age let's say) also varies across
> countries? You can include that variable in the random part of the command
> to let the slope be a random variable as well. So for example, going back to
> your syntax, assume that you believe the coefficient on x2 to be random as
> well, you can type:
> 
> xtmixed depression x1 x3 || country: x2
> 
> Best,
> 
> Alfonso Sánchez-Peñalver, PhD
> 
> Visiting Assistant Professor
> Suffolk University
> Senior Instructor
> UMass Boston
> 
> 
> 
> On Jan 30, 2014, at 3:09 PM, Antonio Rodriguez Andres
> <[email protected]> wrote:
> 
>> Alfonso
>> 
>> Thank you for your answer.  On this way, can I estimate the fixed 
>> effects for each country? What do they mean by random slopes for all data?
>> This can be done using the xtmixed or gllamm command?
>> 
>> 
>> 
>> -----Original Message-----
>> From: [email protected]
>> [mailto:[email protected]] On Behalf Of Alfonso 
>> Sánchez-Peñalver
>> Sent: Thursday, January 30, 2014 9:58 PM
>> To: Stata List
>> Subject: Re: st: gllamm or xtmixed models?
>> 
>> Hola Antonio,
>> 
>> I believe the correct syntax for the random effects model estimated 
>> via maximum likelihood would be
>> 
>> xtmixed depression x1 x2 x3 || country:
>> 
>> Alfonso Sánchez-Peñalver, PhD
>> 
>> Visiting Assistant Professor
>> Suffolk University
>> Senior Instructor
>> UMass Boston
>> 
>> 
>> 
>> On Jan 30, 2014, at 2:52 PM, Antonio Rodriguez Andres 
>> <[email protected]> wrote:
>> 
>>> Dear stata users
>>> 
>>> I want to estimate multilevel models as I have observations for 
>>> individuals across countries.  My dependent variable İs a measure of 
>>> mental health ranging from 0 to 24. I want to use hierarchical linear 
>>> models with fixed effects and random effects for countries. The 
>>> correct syntax is:
>>> 
>>> xtmixed depression   x1 x2 x3   || i(country)
>>> 
>>> Any clue
>>> 
>>> Regards
>>> 
>>> Antonio
>>> 
>>> 
>>> 
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