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Here's a statement I read from the method section in a paper: "One disadvantage of the fixed-effects approach is that the results obtained are conditional on the data used to estimate them; that is, results cannot be generalized to other years or microregions not included in the study."

I think I haven't read a similar comment on fixed effects model elsewhere so want to know if that argument is true. If so, is it a disadvantage of fixed effects versus random effects model?

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    $\begingroup$ I also would not know what would make FE results have any more or less external validity than those of RE or any other empirical technique. $\endgroup$ – Christoph Hanck May 23 '15 at 21:25
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Well the statement is true for all statistics, since a statistic is (but) a function of the data. Converserly, I know of no statistical model/method that overcome the problem.

It a generally a big no-no in statistics to generalize to the entire population, unless you can somehow argue that your sample is "very" good.

On a side note; I have seen some reserachers, within machine learning, who leave out some of the data when they estimate. Then they try to predict the outcome of these (leftout) observations. As a way of testing the the predictive power.

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