Although there are doubtless many techniques for studying the impact of a discrete intervention over time, I am interested in two which have achieved widespread adoption in the social sciences:
- Interrupted time series
The former applies all the principles of time series analysis, using ARIMA models to account for non-stationarity, autocorrelation, etc. The latter uses linear regression or variants, but allows for a control group.
If anything, difference-in-difference is more widely adopted, particularly in the economic literature (after Card, Angrist, etc.) but also in health-related fields and possibly education (where it seems to be called "comparative interrupted time series"). Yet in the presence of substantial autocorrelation (likely, in the types of datasets being used), it would seem to over-state the precision with which results are estimated.
My questions are thus:
- How are difference-in-difference analyses valid if autocorrelation exists?
- Is there a time-series technique that allows a control group to be utilized?