Hi everyone,
Hi dear, my estimation model looks like following:
Yijkt = β0 + β1 ∗ treat_j + β2 ∗ post_t + β3 ∗ treat_j ∗ post_t + β4X_it +β5y_ijk,t-1+ ξ_i + δ_t + ψ_k + ϵ_ijkt
where treat equals 1 if in the treatment group, post equals 1 if the year is equal to or greater than 2012 (policy takes effect in 2012), and X_it are city level time varying variables. ξ_i is the city fixed effect, δ_t is the time fixed effect, and ψ_k is the product fixed effect.
I am running a regression ivreghdfe sales_spec did_1 treat_1 $X1list (lag_sales=lag_price), absorb(i.code i.year i.product) vce(cl province) and ivreghdfe sales_spec did_1 treat_1 $X1list (lag_sales=lag_price), absorb(i.code i.year i.product,savefe) separately, however,the results are very strange:

My question is, why does the first regression show an insufficient number of observations? because there isn't a singleton here.My confusion with the second regression result is why there is a negative centered R square.
Hi dear, my estimation model looks like following:
Yijkt = β0 + β1 ∗ treat_j + β2 ∗ post_t + β3 ∗ treat_j ∗ post_t + β4X_it +β5y_ijk,t-1+ ξ_i + δ_t + ψ_k + ϵ_ijkt
where treat equals 1 if in the treatment group, post equals 1 if the year is equal to or greater than 2012 (policy takes effect in 2012), and X_it are city level time varying variables. ξ_i is the city fixed effect, δ_t is the time fixed effect, and ψ_k is the product fixed effect.
I am running a regression ivreghdfe sales_spec did_1 treat_1 $X1list (lag_sales=lag_price), absorb(i.code i.year i.product) vce(cl province) and ivreghdfe sales_spec did_1 treat_1 $X1list (lag_sales=lag_price), absorb(i.code i.year i.product,savefe) separately, however,the results are very strange:
My question is, why does the first regression show an insufficient number of observations? because there isn't a singleton here.My confusion with the second regression result is why there is a negative centered R square.
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