Hi there,
I am currently conducting a difference-in-difference analysis for my policy study. The policy in question was initially implemented in 2014, relaxed in 2017, and then reinstated in 2020. I have aggregate data at the city level, and it is not a randomized sample. I believe there is a lingering effect of the 2014 policy. The parallel trend assumption holds before 2014, the treated group remains unchanged, and the design is sharp. I have reviewed various studies on different difference-in-difference methodologies for various contexts. However, I have not found one that precisely matches the characteristics of my data. In addition to estimating the Average Treatment Effect on the Treated (ATT), I am also considering estimating the overall Average Treatment Effect (ATE).
I would greatly appreciate your input on the methodologies and references that I can delve into.
Thanks
I am currently conducting a difference-in-difference analysis for my policy study. The policy in question was initially implemented in 2014, relaxed in 2017, and then reinstated in 2020. I have aggregate data at the city level, and it is not a randomized sample. I believe there is a lingering effect of the 2014 policy. The parallel trend assumption holds before 2014, the treated group remains unchanged, and the design is sharp. I have reviewed various studies on different difference-in-difference methodologies for various contexts. However, I have not found one that precisely matches the characteristics of my data. In addition to estimating the Average Treatment Effect on the Treated (ATT), I am also considering estimating the overall Average Treatment Effect (ATE).
I would greatly appreciate your input on the methodologies and references that I can delve into.
Thanks
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