Re: st: panel data analysis

[Danielle Koopmans <dpmjkoopmans@gmail.com>]

Thanks Austin for this answer! I'm going to use it immediately!

greets Danielle

On Tue, Jun 15, 2010 at 6:55 PM, Austin Nichols <austinnichols@gmail.com> wrote:
> 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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