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@googlemail.com> wrote:
On 28 December 2012 02:16, Allin Cottrell
<cottrell@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!
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)
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"My colleagues in the social sciences talk a great deal about methodology. I prefer to call it style." -- Freeman J. Dyson