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st: Re: other ways in dealing with endogeneity other than IV?
In Austin's excellent Stata Journal (7:4, 2007) article, he discusses
the usefulness of fixed effects models to deal with a certain kind of
'endogeneity', which he describes as correlation between regressor
and error. It is true that in the model
y_it = X_it b + u_it
on panel data, with X strictly exogenous, there will be an
endogeneity problem if there is unobserved heterogeneity that depends
only on who you are (only on i). This arises because the true DGP is
y_it = X_it b + [ a_i + u_it]
and if a_i (individual-specific characteristics) are correlated with
observed X's, the error term is correlated with the regressors and
OLS is inconsistent. That can be dealt with, as he discusses, with
fixed effects, expressing a_i as a parameter to be estimated using
individual-specific dummy variables or the within transformation, a
la -xtreg, fe- or -areg-.
BUT if the original assumption that X is exogenous -- uncorrelated
with u -- is not appropriate, then there is conventional endogeneity
in the equation as well as unobserved heterogeneity. Fixed effects
will deal with the latter, but not the former, and fixed effects will
be just as inconsistent as pooled OLS. That is the point I was
making. In that circumstance you need an IV fixed-effects estimator
such as that implemented by -xtivreg, fe- or -xtivreg2, fe-, or an
estimator implementing the first difference transformation with IV (-
xtivreg, fd- or -xtivreg2, fd-).
Kit Baum, Boston College Economics and DIW Berlin
http://ideas.repec.org/e/pba1.html
An Introduction to Modern Econometrics Using Stata:
http://www.stata-press.com/books/imeus.html
On May 7, 2008, at 02:33 , Austin wrote:
Kit et al.--
A fixed-effects model deals with one very specific type of endogeneity
due to omitted variables that vary only across groups in i() and not
within. Hence the discussion of -xtreg- in
http://www.stata-journal.com/article.html?article=st0136
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