Dear Statalists,
I have a question about whether interacting time dummy (time fixed effects) and individual dummy (individual fixed effects) leads to overfitting. I remember that I saw this method somewhere (but I cannot remember exactly). However, introducing such interactions will make the number of independent variables (parameters to be estimated) explode and even outnumber the number of observations. This results in lacking degree of freedom and overfitting. So can I ask if interacting time dummy and individual dummy is a feasible method?
I have tried estimating a model with such interactions by -xtreg y x1 x2 i.time i.time#i.i, fe- (and also alternatively manually creating the interactions) and get results of within R-sauqre equal to 1.000 and all p-values equal to 0.000. The perfect R-square may be a clear evidence of overfitting, let lone the huge number of independent variables
When I alternatively estimate the model with LSDV -reg y x1 x2 i.time i.i i.time#i.i-, then the returned regression table shows only R-square=1.000 and coefficients of each variable, but all other statistics (e.g. std. err., t, p, and conf. interval) are missing (blank) in the table.
I think that introducing the interaction between time dummy and individual dummy is a wrong practice, but I am not completely if I am right.
Thank you very much
I have a question about whether interacting time dummy (time fixed effects) and individual dummy (individual fixed effects) leads to overfitting. I remember that I saw this method somewhere (but I cannot remember exactly). However, introducing such interactions will make the number of independent variables (parameters to be estimated) explode and even outnumber the number of observations. This results in lacking degree of freedom and overfitting. So can I ask if interacting time dummy and individual dummy is a feasible method?
I have tried estimating a model with such interactions by -xtreg y x1 x2 i.time i.time#i.i, fe- (and also alternatively manually creating the interactions) and get results of within R-sauqre equal to 1.000 and all p-values equal to 0.000. The perfect R-square may be a clear evidence of overfitting, let lone the huge number of independent variables
When I alternatively estimate the model with LSDV -reg y x1 x2 i.time i.i i.time#i.i-, then the returned regression table shows only R-square=1.000 and coefficients of each variable, but all other statistics (e.g. std. err., t, p, and conf. interval) are missing (blank) in the table.
I think that introducing the interaction between time dummy and individual dummy is a wrong practice, but I am not completely if I am right.
Thank you very much
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