Okay, I've sorted out the time dummy problem: omitting one of the X
variables that was only included as part of a strategy of adjusting the
scores on the Y variable to make the results comparable across year was
causing the last time dummy to fall out; it made no difference at all to
the coefficients, SEs or model diagnostics, which is good.
May I come back to (either of) you on my Wald/F-test query? When you say
the F-test score should be multiplied to give the Wald test score,
presumably the multiplier is the number of restrictions in the test? Even
if this is correct, I don't _quite_ get the same P-values:
(After running the GLS RE model on my pooled panel data:)
Test statistic: F(5, 749) = 4.65984, with p-value = 0.000339063
Chi-square(5): area to the right of 23.2992 = 0.000295879
(to the left: 0.999704)
They're close, but not exact!
C
On 28 December 2012 08:52, Clive Nicholas <clivelists(a)googlemail.com> wrote:
On 28 December 2012 02:16, Allin Cottrell <cottrell(a)wfu.edu>
wrote:
> Select a set of regressors that are as near orthogonal as possible
> ;-). Seriously, gretl is better able to handle highly collinear
> regressors than most econometric software; if a regressor is
> dropped, it's probably not possible to estimate its coefficient
> other than via multiple precision arithmetic (which gretl offers,
> but for OLS only).
Okay, that's sensible advice, but with this data, these are the only
variables I have: they were chosen for their relevance, as opposed to their
orthogonality!
> > (2) as there is no LDV, how do I ensure that I select robust SEs that do
> > not correct for autocorrelation?
>
> That seems to me a non-sequitur. Why should the non-inclusion of an
> LDV immunize a model against an autocorrelated error? Rather the
> reverse, I would think: if you include enough lags of the dependent
> variable, then maybe you have "whitened" the error term to the point
> where correction for autocorrelation is redundant.
There is no LDV in the model because - correct me if I'm wrong - that
would render the parameter estimates biased in a fixed-effects LSDV model
(Judson and Owen, 1999). The solution to that would be to move to IV
models, but (a) they're not for kids; and (b) I've not yet hammered out a
requisite battery set of IVs to even think about making such a model work!
>
Allin Cottrell
> _______________________________________________
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> Gretl-users(a)lists.wfu.edu
>
http://lists.wfu.edu/mailman/listinfo/gretl-users
>
--
Clive Nicholas (
clivenicholas.posterous.com)
[Please DO NOT mail me personally here, but at <clivenicholas(a)hotmail.com>.
Please respond to contributions I make in a list thread here. Thanks!]
Judson RA and Owen AL (1999) "Estimating dynamic panel data models: a
guide for macroeconomists", Economics Letters 65(1), 9-15
--
Clive Nicholas (
clivenicholas.posterous.com)
[Please DO NOT mail me personally here, but at <clivenicholas(a)hotmail.com>.
Please respond to contributions I make in a list thread here. Thanks!]
"My colleagues in the social sciences talk a great deal about methodology.
I prefer to call it style." -- Freeman J. Dyson