>
> > ... You can treat
> >regressors as non-stochastic if you have control over it. So, it seems to
> >me that the only case when you can treat regressors as fixed is when your
> >data is coming from some designed experiment. I do not know what is your
> >field of study, but if it's social science then you have a problem. In
> >social science most of the data is measurement of uncontrolled (by
> >researcher) processes and cannot be treated as fixed.
>
> What do you mean by "cannot"? What is it that goes wrong? Are you
> saying that the model will not make good predictions for new data from
> the same source? If so, I think you are wrong. Or are you saying
> that you won't be able to make conclusions about causal influences?
> That might well be, but for that, it's not really just a matter of
> "fixed" versus "stochastic".
>
When I say that you cannot treat regressors as fixed I mean following.
Suppose Y=consumption, X=GDP then E(inv(X'X)X'Y) is not equal to
inv(X'X)X'E(Y) since both X and Y are random variables, and you need a
little bit different treatment of
regression. So, "mechanics" of OLS changes a little bit, and of course,
interpretation of regression is different.
=================================================================
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
http://jse.stat.ncsu.edu/
=================================================================
