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Conterfactual analysis with artificial controls: inference, high dimensions and nonstationarity
Recently, there has been growing interest in developing statistical tools to conduct counterfactual analysis with aggregate data when a single ``treated’‘ unit suffers an intervention, such as a policy change, and there is no obvious control group. Usually, the proposed methods are based on the construction of an artificial counterfactual from a pool of ``untreated’‘ peers, organized in a panel data structure. In this paper, we consider a general framework for counterfactual analysis for high dimensional, non-stationary data with either deterministic and/or stochastic trends, which nests well-established methods, such as the synthetic control. Furthermore, we propose a resampling procedure to test intervention effects that does not rely on post-intervention asymptotics and that can be used even if there is only a single observation after the intervention. A simulation study is provided as well as an empirical application where the effects of price changes on the sales of a product are measured.
Link to paper: papers.ssrn.com/sol3/papers.cfm?abstract_id=3303308
Please sign up for meetings here: docs.google.com/spreadsheets/d/1qPQqXivNYBDNJY_0OdHcZfjslHLu5UtSVQMd0LpETqc/edit#gid=0
Date:
29 November 2019, 14:15
Venue:
Manor Road Building, Manor Road OX1 3UQ
Venue Details:
Seminar Room C
Speaker:
Marcelo Medeiros (Pontifical Catholic University of Rio de Janeiro)
Organising department:
Department of Economics
Part of:
Nuffield Econometrics Seminar
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Melis Clark
