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st: RE: xtlogit chamberlain sector-fixed effects


From   "Rodrigo Alfaro A." <ralfaro@bcentral.cl>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: xtlogit chamberlain sector-fixed effects
Date   Thu, 23 Oct 2008 14:45:03 -0300

///

Fardad,

Well, you could add dummies to any problem, but 100 parameters could
give you a nonsense result if your sample size is small. There is a good
paper by Greene that covers this
(http://www.stern.nyu.edu/~wgreene/nonlinearfixedeffects.pdf). Maybe you
will need to find a correspondece between panel and your problem. 

I think that your Chamberlian's model refer to -clogit- or -xtlogit,
fe-. A good econometrician that has more than one paper. Anyway, you
should read in the manual the assumptions of that model. In general is a
nice solution for the problem, but it can be implemented only in logit
model. In other words, we know that binary variables are 0 or 1, but we
are not sure that the model behind is logit, probit or any probabilistic
model. 

Good luck

Rodrigo.
 

-----Mensaje original-----
De: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] En nombre de Fardad Zand
Enviado el: Jueves, 23 de Octubre de 2008 12:09 p.m.
Para: statalist@hsphsun2.harvard.edu
Asunto: st: xtlogit chamberlain sector-fixed effects

Dear Listers,

I'm trying to estimate a model using a cross-section dataset: y_ij =
f(x_ij,u_j,e_ij), where y_ij is a binary, x_ij are regressors at the
firm-level (firm i in sector j), u_j is unobservable sector-specific
effects and e_ij are unobservable firm-specific effects. One way to
capture as mush as u_j in the model is to add separate dummies into the
model. But I have around 100 sectors and adding about 100 dummies
doesn't seem to be a good choice. Moreover, I've heard that dummies
can't be added to logistic specifications as they can in OLS models.

Q1: Theoretically, I'm not sure if one can't add dummies to a logistic
regression as she does in an OLS.

Another recommended approach is to put constraints on the log-likelihood
function in order to sweep out the u_j. One way suggested by Chamberlain
(1980) is to condition the log-likelihood function on Sigma(y_ij).

Q2: Is there any advantages for tis approach compared to adding separate
dummies into the model?

Q3: Do you any better, similar approach for this purpose?

I have been recommended to use Stata -xtlogit- FE with constraints to
run the second approach.

Q4: I couldn't figure it out why -xtlogit- and not -logit- as I'm not
using a panel but a cross-section

Q5: How can I define and use the constraint suggested by Chamberlain?
Can you probably post me the code, as I tried many formulations on my
own but neither actually worked out.

Thank you so much for your time and support.

Good luck all,
Fardad

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