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st: Re: Fixed effects and stable variables: xtivreg or ivreg2?


From   "Rodrigo A. Alfaro" <ralfaro76@hotmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Re: Fixed effects and stable variables: xtivreg or ivreg2?
Date   Mon, 5 Jun 2006 01:29:27 -0400

Dear Jon,

(1) If your variable has some variation over time, then the within variation 
is small. As long as the within variation converges to a positive number, 
you can obtain a fixed-effects estimator for that variable. This estimator 
is consistent, but the standard error is huge. You can quickly see the 
sample within-variation using -xtsum-. I haven't seem the definition of 
highly stable that you provide, could you give me that reference?

I am not sure what you suggest with the autocorrelation value of that 
variable. If you have an autocorrelated variable [pretend AR1: x(i,t) = 
c(i)+r*x(i,t-1)+e(i,t)] the variance of x increase with r. So far, I haven't 
found a solution for a small time-variation variable, but it seems that FE 
are still good estimators.

(2) I reply your question with other: why you want to use instruments? Do 
you have another variable that is correlated with the error term? with FE 
you already drop the cross-sectional unobservable.

Rodrigo.


----- Original Message ----- 
From: "Jon O'Brien" <jobrien2@nd.edu>
To: <statalist@hsphsun2.harvard.edu>
Sent: Sunday, June 04, 2006 11:51 PM
Subject: st: Fixed effects and stable variables: xtivreg or ivreg2?


Hello Statalist members,

I tried this message 2x before and it didn't go through - hopefully 3rd
times a charm! I have 2 related questions. First question:

When using fixed effects, variables that are invariant over time will get
washed out by the firm/subject level fixed effect. I've read that variables
that are highly stable (i.e., show very little variation over time) may also
largely get washed out by the fixed effect. However, I cannot find any
references that provide guidelines for determining when a [key theoretical]
variable is "too stable" to be accurately estimated via fixed effects. For
example, if I have 10 years of annual data and a key variable is correlated
0.91 with it's lag, is that "too stable"?

Second, if a Hausman test suggests that random effects are inappropriate,
what might be a reasonable alternative to xtivreg with the fe option (note:
the stable variable is not the endogenous one)? Would ivreg2 with gmm and
cluster options be reasonable in this case ?

Thanks for any suggestions you can offer,

Jon O'Brien

Assistant Professor

University of Notre Dame
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