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Re: st: cluster
Austin Nichols <email@example.com>
Re: st: cluster
Thu, 28 Feb 2013 10:47:05 -0500
Elin Vimefall <Elin.Vimefall@oru.se>:
for a link to details at [P] _robust, especially page 373-4.
On Thu, Feb 28, 2013 at 3:29 AM, Elin Vimefall <Elin.Vimefall@oru.se> wrote:
> Thanks a lot!
> The references really helped me.
> However; I still have one question which I can not solve.
> When writing the formula for the robust (clustered) variance estimator I want to incorporate weights. Does anyone know how to do that?
> Best regards
> /Elin Vimefall
> -----Original Message-----
> From: firstname.lastname@example.org [mailto:email@example.com] On Behalf Of Austin Nichols
> Sent: den 27 februari 2013 16:17
> To: firstname.lastname@example.org
> Subject: Re: st: cluster
> Elin Vimefall <Elin.Vimefall@oru.se>:
> Where in reading about clustered SEs did you see a reference to fixed-effects probit? If you look at -help xtprobit- or http://www.stata.com/help.cgi?xtprobit
> you will see that
> "There is no command for a conditional fixed-effects model, as there does not exist a sufficient statistic allowing the fixed effects to be conditioned out of the likelihood. Unconditional fixed-effects probit models may be fit with probit command with indicator variables for the panels. However, unconditional fixed-effects estimates are biased."
> That bias is often overstated, and is probably not severe at all if there are many observations per panel. I think you probably have many children per district, and can safely include district dummies. You should probably also report the results of a linear model with and without district dummies. In all of these cases, you should use the cluster option if you have a large number of clusters.
> In general, a fixed-effects (FE) model can reduce bias in estimated coefficients while inflating SEs appropriately when panels have different intercepts u_i (kids in different districts have different intrinsic likelihoods to be in school in your setting) that are correlated with other characteristics X_i. Random effects (RE) estimators do not address this bias, but can improve efficiency of estimates. Both can increase bias due to measurement error in predictors.
> The reason to cluster SEs is not to get better estimated coefficients, but to get better estimated variability of the estimated coefficients, to improve inference without altering the estimated coefficients.
> Cluster-robust SEs are clearly better if you have a large number of clusters (districts, in your case), relative to the number of coefs/restrictions you want to test, but can perform poorly with a small number of clusters: see e.g.
> On Wed, Feb 27, 2013 at 6:53 AM, Elin Vimefall <Elin.Vimefall@oru.se> wrote:
>> Dear list members
>> To analyze which children that are enrolled in school I use a probit
>> model. To control for the fact that the error terms are correlated at
>> district level I use cluster()
>> Probit school x1 x2..., cluster(district)
>> However; I do not really understand how the cluster() works. When reading about clustering I understand that you can do both fixed effects and random effects. How can I do that in stata, and which of them do cluster do?
>> Best regards /Elin Vimefall
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