A week ago I already asked a question to my first project, unfortunately it was not realy clear what I wanted. Now I am a few steps further..
What I want to know is if my model is correct. It's a mix of difference-in-differences and Fixed Effects
I want to measure the "(medium-term)-effect of smoke cessation on BMI".. the panel data set includes the questions about bmi and smoking every other year 2002, 2004, ... , 2012 By "medium term" I mean that I am also interessted how the weight gain looks in 2 years, 4 years and maybe in 6 years
I droped a few oberservation and have three groups
a) Neversmokers.. they never smoked
b) Ex-smoker.. they smoked 2002, 2004, 2006 and did not smok ein 2008, 2010, 2012 (the exact moment when they stopped it not clear)
c) Alwayssmokers.. they always smoked
First of all I plotted the average BMIs of the groups and the results are what I expected, "smokers have lower bmi and after smoke cessation people gain weight"
Then I tried normal D-i-D, but I am afraid that the results may be wrong because the treatment is endogenous, its not a political policy, people decide to stop smoking
So I did Fixed Effects:
// did = post2006 * exsmokerdummy xtreg bmi did i.year manyvariables, robust fe cluster(id) xtreg bmi_t2 did i.year manyvariables, robust fe cluster(id) xtreg bmi_t4 did i.year manyvariables, robust fe cluster(id)
The results look promising: did coefficients are 1.01, 0.74 and 0.034 (all significant) which I guess are similiar to the results in the picture below But the R-squared overall is only 0.0049
So do you think the model might show this effect I am looking for? I welcome every answer.