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Re: st: -xtreg,fe-, whether to drop out individuals participated once
Ekaterina Selezneva wrote:
> There is a 5 round panel datset, which is unbalanced. Some cases contain
> missing values for several variables (in the variable of interest as well).
> Let's assume, I want to estimate something like -xtreg y x* time*,fe i(id)-
> if I exploit the unbalanced dataset "as it is", Stata estimates the model
> showing a number of observations per group being from 1 to 5. (in the
> dataset used in Stata 9 manual it is from 1 to 15).
> Reading a panel econometrics textbook, one would find (for example, in
> Wooldridge, p.267) a rather logical phrase like "when at least two time
> periods available,... " before the formulas of the fixed effects
> If following this logic, I would need to drop all the individuls, who
> participated just once and than estimate the model. If I do so, the
> estimate results are slightly different.
> Am I right, that it happends due to the fact that in -xtreg,fe- Stata not
> only substracts the individual means, but also the 'pooled sample mean'?
> What would be the best strategy to choose: leave it as it is, or drop those
> participated once?
> And if the answer is the second option.. should I drop out those two have
> just one anwser to my question of interest, OR, at Stata performs in the
> complete cases analysis style, I need leave in my sample just those people
> who have answered the question at least twice, and moreover, only if they
> have complete information for all the explanatory variables.
There might be a difference in the constant and in sigma_u, but there
shouldn't be a difference in the coefficients of the independent variables.
In a fixed effects model the 1-observation-only respondents do not contribute
to the calculation of the coefficients. As long as you only interpret the
coefficients of the independent variables it really doesn't matter whether
your drop these observations, or not,
The sigma_u is the standard deviance of the dummies for the individuals. You
can interpret this statistic as a measure for how much the individuals differ
in their average value of the dependent variable. If you want to interpret it
that way it is your choice wheter you want to drop the 1-observation-only
respondents, or not. If you only want to talk about people, who are observed
more than once, drop them.
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