I have a research project trying to investigate the impact of a policy shock, and the shock occurred at different times for different individuals -- the staggered rollout pattern makes me think about using generalized DiD framework, Y(it) = a X(it)+..., where X(it) is the dummy which is 1 after the policy shock.
However, what is special about my dataset is that the treatment is not always "on" -- after the first policy occurs, the policy may disappear in some years. I am wondering is it still suitable to use it as a quasi-experiment in the generalized DiD analysis? Or are there more suitable models to analyze this type of dataset?
The panel data looks like
Company | Year | Treatment | Dependent Variable |
1 | 2010 | 1 | |
1 | 2011 | 0 | |
1 | 2012 | 1 | |
2 | 2009 | 0 | |
2 | 2010 | 1 | |
2 | 2011 | 1 | |
2 | 2012 | 0 | |
3 | 2011 | 1 | |
3 | 2012 | 0 |
0 Response to Question about Generalized DID
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