I have a practical issue concerning the use of dummy variables in a difference-in-difference model with fixed effects. Usually, all dummies that a collinear with the fixed effects will be omitted and their coefficient and significance cannot be interpreted in the regression output. But I regularly view papers which the treatment dummy in DiD models with fixed effects that to my understanding ought to get omitted. See this paper (https://www.sciencedirect.com/scienc...72308914000485) where the treatment dummy "affected bank" in table 3 on p. 274 is reported despite the use of individual fixed effects.
Regarding my regressions, I observe some inconsistencies with the time dummy for periods after the treatment applies. I estimate the following difference-in-difference model using individual and time fixed effects (abbreviated code below) and as robustness country and time fixed effects:
Code:
xtreg depvar indepvar i.post15 i.treated i.post15##i.treated controls i.year, fe vce(cluster idno)
Code:
----------------------------------------------------------------------------------- | Robust depvar | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------------+---------------------------------------------------------------- 1.post15 | .5214625 .0472556 11.03 0.000 .4286728 .6142523 1.treated | 0 (omitted) | post15#treated | 1 1 | .6011524 .1515873 3.97 0.000 .3035001 .8988048 | yr2 | 2011 | .0337759 .0125834 2.68 0.007 .0090674 .0584844 2012 | .0820242 .0163258 5.02 0.000 .0499674 .114081 2013 | .1399963 .0164042 8.53 0.000 .1077855 .172207 2015 | -.0378953 .0176233 -2.15 0.032 -.0724999 -.0032907 2016 | -.0232412 .016611 -1.40 0.162 -.055858 .0093756 2017 | .0219256 .0145132 1.51 0.131 -.0065721 .0504232 2018 | 0 (omitted) | _cons | 3.72202 .0229494 162.18 0.000 3.676957 3.767082 -------------------+---------------------------------------------------------------- sigma_u | 1.1245096 sigma_e | .60822459 rho | .77366385 (fraction of variance due to u_i) ------------------------------------------------------------------------------------
Code:
xtreg depvar indepvar i.year i.post15 i.treated i.post15##i.treated controls, fe vce(cluster id)
Code:
----------------------------------------------------------------------------------- | Robust depvar | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------------+---------------------------------------------------------------- yr2 | 2011 | .0337759 .0125834 2.68 0.007 .0090674 .0584844 2012 | .0820242 .0163258 5.02 0.000 .0499674 .114081 2013 | .1399963 .0164042 8.53 0.000 .1077855 .172207 2015 | .4835672 .0454511 10.64 0.000 .3943209 .5728136 2016 | .4982213 .0461855 10.79 0.000 .4075328 .5889099 2017 | .5433881 .0452527 12.01 0.000 .4545313 .6322449 2018 | .5214625 .0472556 11.03 0.000 .4286728 .6142523 | 1.post15 | 0 (omitted) 1.treated | 0 (omitted) | post15#govsupp2_50 | 1 1 | .6011524 .1515873 3.97 0.000 .3035001 .8988048 | _cons | 3.72202 .0229494 162.18 0.000 3.676957 3.767082 -------------------+---------------------------------------------------------------- sigma_u | 1.1245096 sigma_e | .60822459 rho | .77366385 (fraction of variance due to u_i) ------------------------------------------------------------------------------------
When I consider the cited paper above where a treated dummy is reported in a model with individual fixed effects, I wonder how the authors performed the regressions. I can think of the following code for multiple levels of fixed effects:
Code:
reghdfe depvar indepvar i.year i.post15 i.treated i.post15##i.treated controls, absorb(id year) vce(cluster id)
Q2: How to estimate a treatment dummy (for individuals who receive treatment =1, otherwise =0) with individual fixed effects.
I would appreciate your help.
Regards,
Julian
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