4 replaced http://stats.stackexchange.com/ with https://stats.stackexchange.com/ edited Apr 13 '17 at 12:44 The DDD formula is slightly different than both of those. It is $$Y = \alpha + a + b + c + a*b + a*c + b*c + a*b*c + controls + \epsilon$$ where $$\alpha$$ is the intercept, and $$\epsilon$$ is the is the error term. That is, you also have to include the main effects, $$a$$, $$b$$, and $$c$$. Assuming you are examining a single event that occurs at a point in time, one of these three main effects could be modeled by year dummies. See equation 1.3 on page 2 of the Wooldridge Imbens lecture and a related post on CV herehere. In one approach, the Stata command would be something like reg y i.a##i.b##i.c controls, vce(cluster clustvarname)  $$c_i$$ is unobserved heterogeneity of the observational unit, while $$\lambda_t$$ is unobserved heterogeneity of time. In the basic two-period model, $$\lambda_t$$ is a binary variable, but in a multi period environment, you have the ability to more flexibly model time. You would also have a third aspect of unobserved heterogeneity. These (the main effects) can be modeled by adding fixed effects, but for a DDD, you will also need to model the interaction (second order) effects and of course include a term for your triple interaction (the variable of interest). The DDD formula is slightly different than both of those. It is $$Y = \alpha + a + b + c + a*b + a*c + b*c + a*b*c + controls + \epsilon$$ where $$\alpha$$ is the intercept, and $$\epsilon$$ is the is the error term. That is, you also have to include the main effects, $$a$$, $$b$$, and $$c$$. Assuming you are examining a single event that occurs at a point in time, one of these three main effects could be modeled by year dummies. See equation 1.3 on page 2 of the Wooldridge Imbens lecture and a related post on CV here. In one approach, the Stata command would be something like reg y i.a##i.b##i.c controls, vce(cluster clustvarname)  $$c_i$$ is unobserved heterogeneity of the observational unit, while $$\lambda_t$$ is unobserved heterogeneity of time. In the basic two-period model, $$\lambda_t$$ is a binary variable, but in a multi period environment, you have the ability to more flexibly model time. You would also have a third aspect of unobserved heterogeneity. These (the main effects) can be modeled by adding fixed effects, but for a DDD, you will also need to model the interaction (second order) effects and of course include a term for your triple interaction (the variable of interest). The DDD formula is slightly different than both of those. It is $$Y = \alpha + a + b + c + a*b + a*c + b*c + a*b*c + controls + \epsilon$$ where $$\alpha$$ is the intercept, and $$\epsilon$$ is the is the error term. That is, you also have to include the main effects, $$a$$, $$b$$, and $$c$$. Assuming you are examining a single event that occurs at a point in time, one of these three main effects could be modeled by year dummies. See equation 1.3 on page 2 of the Wooldridge Imbens lecture and a related post on CV here. In one approach, the Stata command would be something like reg y i.a##i.b##i.c controls, vce(cluster clustvarname)  $$c_i$$ is unobserved heterogeneity of the observational unit, while $$\lambda_t$$ is unobserved heterogeneity of time. In the basic two-period model, $$\lambda_t$$ is a binary variable, but in a multi period environment, you have the ability to more flexibly model time. You would also have a third aspect of unobserved heterogeneity. These (the main effects) can be modeled by adding fixed effects, but for a DDD, you will also need to model the interaction (second order) effects and of course include a term for your triple interaction (the variable of interest). 3 added 690 characters in body edited May 26 '16 at 19:24 lmo 66711 gold badge66 silver badges1515 bronze badges The DDD formula is slightly different than both of those. It is $$Y = \alpha + a + b + c + a*b + a*c + b*c + a*b*c + controls + \epsilon$$ where $$\alpha$$ is the intercept, and $$\epsilon$$ is the is the error term. That is, you also have to include the main effects, $$a$$, $$b$$, and $$c$$. Assuming you are examining a single event that occurs at a point in time, one of these three main effects could be modeled by year dummies. See equation 1.3 on page 2 of the Wooldridge Imbens lecture and a related post on CV here. In one approach, the Stata command would be something like reg y i.a##i.b##i.c controls, vce(cluster clustvarname)  $$c_i$$ is unobserved heterogeneity of the observational unit, while $$\lambda_t$$ is unobserved heterogeneity of time. In the basic two-period model, $$\lambda_t$$ is a binary variable, but in a multi period environment, you have the ability to more flexibly model time. You would also have a third aspect of unobserved heterogeneity. These (the main effects) can be modeled by adding fixed effects, but for a DDD, you will also need to model the interaction (second order) effects and of course include a term for your triple interaction (the variable of interest). The DDD formula is slightly different than both of those. It is $$Y = \alpha + a + b + c + a*b + a*c + b*c + a*b*c + controls + \epsilon$$ where $$\alpha$$ is the intercept, and $$\epsilon$$ is the is the error term. That is, you also have to include the main effects, $$a$$, $$b$$, and $$c$$. Assuming you are examining a single event that occurs at a point in time, one of these three main effects could be modeled by year dummies. See equation 1.3 on page 2 of the Wooldridge Imbens lecture and a related post on CV here. The DDD formula is slightly different than both of those. It is $$Y = \alpha + a + b + c + a*b + a*c + b*c + a*b*c + controls + \epsilon$$ where $$\alpha$$ is the intercept, and $$\epsilon$$ is the is the error term. That is, you also have to include the main effects, $$a$$, $$b$$, and $$c$$. Assuming you are examining a single event that occurs at a point in time, one of these three main effects could be modeled by year dummies. See equation 1.3 on page 2 of the Wooldridge Imbens lecture and a related post on CV here. In one approach, the Stata command would be something like reg y i.a##i.b##i.c controls, vce(cluster clustvarname)  $$c_i$$ is unobserved heterogeneity of the observational unit, while $$\lambda_t$$ is unobserved heterogeneity of time. In the basic two-period model, $$\lambda_t$$ is a binary variable, but in a multi period environment, you have the ability to more flexibly model time. You would also have a third aspect of unobserved heterogeneity. These (the main effects) can be modeled by adding fixed effects, but for a DDD, you will also need to model the interaction (second order) effects and of course include a term for your triple interaction (the variable of interest). 2 added 97 characters in body edited May 25 '16 at 12:40 lmo 66711 gold badge66 silver badges1515 bronze badges The DDD formula is slightly different than both of those. It is $$Y = \alpha + a + b + c + a*b + a*c + b*c + a*b*c + \delta_t +controls + \epsilon$$$$Y = \alpha + a + b + c + a*b + a*c + b*c + a*b*c + controls + \epsilon$$ where $$\alpha$$ is the intercept, $$\delta_t$$ are the year dummies, and $$\epsilon$$ is the is the error term. That is, you also have to include the main effects, $$a$$, $$b$$, and $$c$$. Assuming you are examining a single event that occurs at a point in time, one of these three main effects could be modeled by year dummies. See equation 1.3 on page 2 of the Wooldridge Imbens lecture and a related post on CV here. The DDD formula is slightly different than both of those. It is $$Y = \alpha + a + b + c + a*b + a*c + b*c + a*b*c + \delta_t +controls + \epsilon$$ where $$\alpha$$ is the intercept, $$\delta_t$$ are the year dummies, and $$\epsilon$$ is the is the error term. That is, you also have to include the main effects, $$a$$, $$b$$, and $$c$$. See equation 1.3 on page 2 of the Wooldridge Imbens lecture and a related post on CV here. The DDD formula is slightly different than both of those. It is $$Y = \alpha + a + b + c + a*b + a*c + b*c + a*b*c + controls + \epsilon$$ where $$\alpha$$ is the intercept, and $$\epsilon$$ is the is the error term. That is, you also have to include the main effects, $$a$$, $$b$$, and $$c$$. Assuming you are examining a single event that occurs at a point in time, one of these three main effects could be modeled by year dummies. See equation 1.3 on page 2 of the Wooldridge Imbens lecture and a related post on CV here. 1 answered May 25 '16 at 12:28 lmo 66711 gold badge66 silver badges1515 bronze badges