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Re: st: ivreg2, cluster vs. state fixed effects


From   Duha Altindag <daltin2@tigers.lsu.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: ivreg2, cluster vs. state fixed effects
Date   Thu, 11 Feb 2010 13:00:24 -0600

You can use egen [new_var_name]=group(state year) to create groups for
state-years.

On Thu, Feb 11, 2010 at 6:28 AM, Nirina F <fstata@gmail.com> wrote:
>
> Dear all,
> I am estimating a 2SLS for the following equation from a microdata at
> individual level:
>
> Y = b0+ b1*X1 +X2 ' *b2
> where Y and X1 are dummy variables and X1 is endogenous and will be
> instrumented with Z. X2 is a vector of control variables.
>
> I only have one instrument and it is from a state level data because
> it is the number of hospitals that the individual has in her state.
> Therefore, I cannot use state-fixed effects anymore as otherwise, Z
> will get dropped automatically due to collinearity.
> Therefore, the model isn't identified with state effects, because
> implicitly, I am using state as IV.
> I am thinking of clustering the standard errors on state, so am I
> right to just run the following?
>
> ivreg2 y (x1=z) x2, cluster (state)
>
> I tried to put under cluster state dummies but  I realized that I can
> only put one variable under cluster.
> So I am wondering how do people cluster by region-year level? because
> if we just  gen a variable
> gen regyr=region*year and then put that variable under cluster then we
> might get trapped in the magic of multiplication.
> suppose my region is coded from 1 to 4 and year from 1 to 5, then
> 2*3=3*2=6 therefore I cannot say those who are from region 2 and born
> in 3 are in the same group as those who are from region 3 and born in
> year 2.
>
>  Also, after clustering my coefficient on b1 became insignificant and
> decreased in value.
>
> This is the results I get from loneway of x1 against z (as may be you
> have other suggestions for me on how to deal with this identification
> problem?)
>
> loneway x1 z
>
>                  One-way Analysis of Variance for x1:
>
>                                              Number of obs =     33385
>                                                  R-squared =    0.1178
>
>    Source                SS         df      MS            F     Prob > F
> -------------------------------------------------------------------------
> Between z           903.19067     23    39.269159    193.61     0.0000
> Within z            6766.5203  33361    .20282726
> -------------------------------------------------------------------------
> Total                  7669.7109  33384    .22974212
>
>         Intraclass       Asy.
>         correlation      S.E.       [95% Conf. Interval]
>         ------------------------------------------------
>            0.12599     0.04266       0.04237     0.20961
>
>         Estimated SD of z effect             .1709937
>         Estimated SD within z                .4503635
>         Est. reliability of a z mean          0.99483
>              (evaluated at n=1336.11)
>
> Thank you in advance for your time and for your help,
>
> Nirina
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