Dear all,
I have a large panel dataset for which I would like to estimate a
regression model of the following structure:
y_{i,t} = a + b * z_{i} + c * x_{i,t} + d * zx_{i,t} + e_{i,t}
where zx_{i,t} is an interaction between dummy variable z_{i} which is
one for individuals from an EMU country and zero otherwise and an
individual specific variable x_{i,t} which varies over time and across
individuals.
Estimating the regression with OLS works fine: . reg y z x zx, cluster(id)
However, there is a risk of endogeneity in the model and I would
therefore like to estimate the regression by aid of fixed effects
regression. However, since there is no within-variation inherent in
variable z_{i}, variable z_{i} will be dropped from the estimation. In
contrast, the interaction term zx_{i,t} is time-varying and will
therefore not be dropped from the within regression.
What I wonder now is whether or not the interaction term may be
interpreted as it is interpreted in the case of pooled OLS (i.e. as if
variable z_{i} would be included in the regression) or if I have to
drop the interaction variable from the
. xtreg y x zx, fe cluster(id)
regression in order to obtain a meaningful regression model.
Thanks for your insights in advance.
Best,
Ingo
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