Hello, everyone!
I'm encounting a confusing problem when running a model including both individual-level variables(age,gender, etc.) and group-level varable(group-level mean). The data is cross-sectional micro-level data(survey data).
I am confusing because when I estimate the model y=a1*x1+a2*x1(bar)+a3*x2 with code reg y x1 x1(hat) x2 i.group (x1(bar) is derived by averaing x1 over the group), i should not have gauged the coefficient of a2. It has no variation across the group, and when controlled the group fixed effect with dummies, how can that coefficient be possibaly estimated?
And how can the estimated coefficient of a2 be interpreted under such condition? other things equal, the same level of x1 and in the same group, one unite increase in the group mean (x1_bar) will lead to a2 unit change in y? how can the group mean change controlling in the same group??
I really do not understand what is wrong as it can be estimated following the above-mentioned code in Stata
Looking forward to some kind responses!
Best regards!
I'm encounting a confusing problem when running a model including both individual-level variables(age,gender, etc.) and group-level varable(group-level mean). The data is cross-sectional micro-level data(survey data).
I am confusing because when I estimate the model y=a1*x1+a2*x1(bar)+a3*x2 with code reg y x1 x1(hat) x2 i.group (x1(bar) is derived by averaing x1 over the group), i should not have gauged the coefficient of a2. It has no variation across the group, and when controlled the group fixed effect with dummies, how can that coefficient be possibaly estimated?
And how can the estimated coefficient of a2 be interpreted under such condition? other things equal, the same level of x1 and in the same group, one unite increase in the group mean (x1_bar) will lead to a2 unit change in y? how can the group mean change controlling in the same group??
I really do not understand what is wrong as it can be estimated following the above-mentioned code in Stata
Looking forward to some kind responses!
Best regards!
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