On Thu, 16 Feb 2012, buehlerman wrote:
Achim Zeileis-4 wrote
The reason for the various approaches is that efp() was always confined to
the linear model and gefp() then extended it to arbitrary estimating
function-based models. And for the linear model this provides the option
of treating the variance of a nuisance parameter or a full model
parameter.
I'm not sure, if my understanding of treating the variance as a nuisance
parameter (in the Nyblom-Hansen test) is right.
The main interest of my analysis relies on the stability of regression
coefficients (and not of the variance). Is it therefore prefereable to
treat the variance as a nuisance parameter rather than a full model
parameter?
Yes.
In that case the variance is estimated to carry out the tests, but the
stability of the variance itself is not assessed.
Best,
Z
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