Say that one has data over time,
t, on an outcome,
y. There is an event that happens at
t==0. One is interested in testing for evidence that the event is related to (I am being cautious on a causal interpretation) a change in the outcome. Importantly, there are many observations for each
t (as opposed to a traditional time series when there is only one observation per
Suppose also that this is not a situation where the event happened in only or some of the units (e.g., states), but that there is only one unit. This rules out an analysis such as a difference in difference as there is no control group.
In a situation like this, should I use a interrupted time series analysis or regression discontinuity design? If both are fine, what are the differences/advantages/disadvantages? (I am also much more familiar with RDD than the interrupted time series; where is a good place to learn about the latter?)