Hello!
I have a two-way interaction model with firm and year fixed effects, where i.treat_post is treat*post, because this model use two-way fixed effects, the individual terms of treat and post are not shown.
I have two other models which further divide the two-way interaction model into high and low subsamples based on whether the sample has above or below median value of R&D expense, using the high_rd dummy to indicate. The two models are as follows
I understand that for the two-way interaction model, all the control variables only estimate a single coefficient, such as ROA is the average effect of ROA for the high and low subsample; whereas for the two subsample tests, the coefficient for ROA in the high subsample is different from that of in the low subsample.
I wonder is there a way to make the coefficients of the control variables the same across models? I think maybe a full interaction model for the two-way interaction can probably do, I have searched but there's not much information available. I wonder how can this be achieved?
Thanks!
I have a two-way interaction model with firm and year fixed effects, where i.treat_post is treat*post, because this model use two-way fixed effects, the individual terms of treat and post are not shown.
Code:
xtreg y i.treat_post employee ROA marketcap TobinsQ FCF i.year ,fe vce(cluster firm)
Code:
xtreg y i.treat_post employee ROA marketcap TobinsQ FCF i.year if high_rd==1 ,fe vce(cluster firm) xtreg y i.treat_post employee ROA marketcap TobinsQ FCF i.year if high_rd==0 ,fe vce(cluster firm)
I wonder is there a way to make the coefficients of the control variables the same across models? I think maybe a full interaction model for the two-way interaction can probably do, I have searched but there's not much information available. I wonder how can this be achieved?
Thanks!
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