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What is the parallel trend assumption?
The parallel trends assumption states that, although treatment and comparison groups may have different levels of the outcome prior to the start of treatment, their trends in pre-treatment outcomes should be the same.
What is the parallel trend assumption in a difference in difference design?
Parallel Trend Assumption It requires that in the absence of treatment, the difference between the ‘treatment’ and ‘control’ group is constant over time. Although there is no statistical test for this assumption, visual inspection is useful when you have observations over many time points.
What is the key assumption of a difference in differences regression?
The key identifying assumption in DD models is that the treatment states have similar trends to the control states in the absence of treatment. With only one treatment and control group, graph your results, and look at trends in periods with before the treatment.
What is the difference between DID and RDD?
DID requires panel data and is more global in some sense. In the extreme case when the number of periods before and after the treatment is very large, we could do an RDD with time as the running variable and the difference between treatment and control groups as the outcome.
What happens if you violate the parallel trend assumption?
Violation of parallel trend assumption will lead to biased estimation of the causal effect. DID is usually implemented as an interaction term between time and treatment group dummy variables in a regression model. Comparison groups can start at different levels of the outcome. (DID focuses on changerather than absolute levels)
How can we test the parallel paths assumption?
When data on several pre-treatment periods exist, researchers like to check the Parallel Paths assumption by testing for differences in the pre-treatment trends of the treatment and comparison groups. Equality of pre-treatment trends may lend confidence but this can’t directly test the identifying assumption; by construction that is untestable.
Is the pre-trend common assumption reasonable in a difference in difference framework?
There is a good manner to verify if the pre-trend common assumption is reasonable in a difference-in-difference framework with two time and two periods. But is necessary to have some data for more than one pre-treatment period (Sometimes, the DiD with two periods performs better than the DiD with multiple periods).
How to report treatment effect of parallel trends?
The idea here is to first report treatment effect estimates from a DiD model with a more complex trend difference than you think to be the case – so if you think there are parallel trends, first estimate a model that allows for the two groups to have linear trends with different slopes by including a linear trend difference.