Date: 07 JUNE 2018 at 14:30
Event location: TBA
Type: Statistics Seminars
Control groups can provide counterfactual evidence for assessing
the impact of an event or policy change on a target variable. We argue
that setting a multivariate time series model offers potential gains
over a direct comparison between the target and a weighted average
of controls. More importantly, it highlights the assumptions underlying
methods such as difference-in-differences and synthetic control,
suggesting ways to test these assumptions. Gains from simple and
transparent time series models are analysed using examples from the
literature, including the California smoking law of 1989 and German
reunification. We argue that selecting controls using a time series
strategy is preferable to existing data-driven regression methods.
KEYWORDS: common trends; di¤erence in di¤erences; intervention
analysis; stationarity tests; synthetic control; unobserved components.
JEL: C22, C23