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Re: st: panel data analysis


From   Austin Nichols <austinnichols@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: panel data analysis
Date   Tue, 15 Jun 2010 12:55:38 -0400

Danielle Koopmans <dpmjkoopmans@gmail.com> :
The R-squared values are of no practical use.  Get -xtivreg2- and
-xtoverid- from SSC, and request clustered SEs in a FE regression,
which address heterosk and autocorr (colinearity is not a concern per
se).  FE is consistent whenever RE is, so it is generally preferred
unless RE is also demonstrably consistent and substantially more
efficient.  -xtoverid- will compare RE and FE; quoting from its help:

A test of fixed vs. random effects can also be seen as a test of
overidentifying restrictions.  The fixed effects
estimator uses the orthogonality conditions that the regressors are
uncorrelated with the idiosyncratic error e_it,
i.e., E(X_it*e_it)=0.  The random effects estimator uses the
additional orthogonality conditions that the regressors
are uncorrelated with the group-specific error u_i (the "random
effect"), i.e., E(X_it*u_i)=0.  These additional
orthogonality conditions are overidentifying restrictions.  The test
is implemented by xtoverid using the artificial
regression approach described by Arellano (1993) and Wooldridge (2002,
pp. 290-91), in which a random effects
equation is reestimated augmented with additional variables consisting
of the original regressors transformed into
deviations-from-mean form.  The test statistic is a Wald test of the
significance of these additional regressors.  A
large-sample chi-squared test statistic is reported with no
degrees-of-freedom corrections.  Under conditional
homoskedasticity, this test statistic is asymptotically equivalent to
the usual Hausman fixed-vs-random effects
test; with a balanced panel, the artificial regression and Hausman
test statistics are numerically equal.  See
Arellano (1993) for an exact statement and the example below for a
demonstration.  Unlike the Hausman version, the
test reported by xtoverid extends straightforwardly to
heteroskedastic- and cluster-robust versions, and is
guaranteed always to generate a nonnegative test statistic.

webuse abdata, clear
xtivreg2 n w k if year>=1978 & year<=1982, fe cl(id)
xtreg n w k if year>=1978 & year<=1982, re robust
xtoverid


On Tue, Jun 15, 2010 at 6:41 AM, Danielle Koopmans
<dpmjkoopmans@gmail.com> wrote:
> Hello,
<snip>
> Fe
>
> R-sq:  within  = 0.2611
>        between = 0.0194
>        overall = 0.0007
>  It doesn't seem good to me these results but which model should I
> choose and which R^2 do I have to look at: within, between or overall?
> My constant is also negtive at the fe model, how come?
>
> And how to check for heteroskedastiscity, serial correlation
> (Durbin-Watson test?) and collinearity?
>
> Hopefully someone can help me on this. This is all very new to me.
> Danielle

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