A Method for Experimental Events that Break Cointegration: Counterfactual Simulation
Peter Bell ()
MPRA Paper from University Library of Munich, Germany
In this paper I develop a method to estimate the effect of an event on a time series variable. The event is framed in a quasi-experimental setting with time series observations on a treatment variable, which is affected by the event, and a control variable, which is not. Prior to the event, the two variables are cointegrated. After the event, they are not. Since the event only affects the treatment variable, the method uses observations on the control variable after the event and the distribution of difference in differences before the event to simulate values for the treatment variable as-if the event did not occur; hence the name counterfactual simulation. I describe theoretical properties of the method and show the method in action with purpose-built data.
Keywords: Quasi-experiment; cointegration; time series; counterfactual; simulation (search for similar items in EconPapers)
JEL-codes: C15 C32 C63 C90 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-sog
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:53523
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