![eststo stata 13 eststo stata 13](https://s3.studylib.net/store/data/006981472_1-cad640d53182a40009ce79f3433d5190-768x994.png)
All the linear/polynomial trends are for the treatment group only, not both. They also have a WP with a section on common types of specifications from the literature. If I understand their notation, that corresponds to i.city#c.treated#c.t or i.city#1.treated#c.t with i.t. There's a nice SJ paper by Mora and Reggio, where they discuss this approach (take a look at equations (3) and (4)). You are going further and making the time trend city-specific in your first command. As a way to relax the parallel trends assumption, people will often include time dummies and a parametric time trend (linear or quadratic) for the treated only in the estimating specification. The second allows for mpg to alter price for foreign cars only. The first spec allows for two separate effects for mpg on price, one for domestic cars and one for foreign. Here's example with the cars data: sysuse auto, clear I will add an example below.$^*$Īlso, I might be inclined to cluster at the city level with this setup.Īlso, note that i.treated#c.time and c.treated#c.time are not equivalent. Fixing the base with something like ib4.city and ib0.time will remedy this. This means the parameters will be different for that reason alone.
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This will not matter in simple models, but once you add interactions, Stata might choose a different city as the base in your specification (for reasons which elude me). I will assume that is what you had intended below. It is strange to me that treated and after are dummies, yet you are treating them as continuous variables by using the c. This is edited a bit in response to revisions. Thanks to Dimitriy for the current answer! Why with a 2-way interaction, treating the variable treated as continuous or as a factor does not matter? Morever, some tests I did suggest that adding i.treated#c.time Can someone explain me the statistical difference between including one term or the other in my regression? Was exactly the same (note that the variable treated in coded as 0/1), but apparently is not. Second, I thought that adding: i.city#c.treated#c.time The first question is whether this approach makes sense. Generally, in this kind of models I use to include only a treatment-specific trend, and not a 3-way interaction. Is the treatment-city specific linear trend. Xtreg depvar i.treated i.after c.treated#c.after i.time i.city#c.treated#c.time, fe cluster(id) To relax the parallel assumption, I include a treatment-city specific time trend - people often include treatment specific trends, but in my settings the outcome can vary a lot across cities for treatment and control units - so my specification should be: xtset id time I am including individual fixed effects and year-month fixed effects. With this data, I am running a simple difference-in-differences model.
![eststo stata 13 eststo stata 13](https://d3i71xaburhd42.cloudfront.net/5cc19d84efea71686a532d5663e188cc3390c159/10-Figure6-1.png)
Where id identifies the individuals in my panel, city is the location where the individual live (non-time varying), treated is a dummy indicating those individual that are eventually treated (0: non-treated, 1: treated), time is a year-month variable, and after is a dummy (0: before, 1: after) indicating the period in which the treated unit are under treatment. I have a panel data in the following form: