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Re: st: ivreg2, cluster vs. state fixed effects
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 <[email protected]> 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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