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Outside the box: using synthetic control methods as a forecasting technique

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  • Stefan Klößner
  • Gregor Pfeifer

Abstract

We introduce synthetic control methods (SCM) as a forecasting technique. Using (i) as economic predictors solely the outcome itself, i.e. lagged values of the dependent variable, and (ii) lagged time series of the outcome to build the donor pool, we let SCM choose and weight appropriate values in order to come up with a sensible forecast of the US GDP growth. This procedure performs competitively viable compared with alternative forecasting methods.

Suggested Citation

  • Stefan Klößner & Gregor Pfeifer, 2018. "Outside the box: using synthetic control methods as a forecasting technique," Applied Economics Letters, Taylor & Francis Journals, vol. 25(9), pages 615-618, May.
    • Handle: RePEc:taf:apeclt:v:25:y:2018:i:9:p:615-618
    DOI: 10.1080/13504851.2017.1352071
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    Cited by:

    1. Nuno Garoupa & Rok Spruk, 2024. "Populist Constitutional Backsliding and Judicial Independence: Evidence from Turkiye," Papers 2410.02439, arXiv.org.
    2. Martin Becker & Stefan Klößner & Gregor Pfeifer, 2018. "Cross-Validating Synthetic Controls," Economics Bulletin, AccessEcon, vol. 38(1), pages 603-609.
    3. Ibrahim Alnafrah & Sulaiman Mouselli, 2020. "Constructing the Reconstruction Process: a Smooth Transition Towards Knowledge Society and Economy in Post-Conflict Syria," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 11(3), pages 931-948, September.
    4. Kaul, Ashok & Klößner, Stefan & Pfeifer, Gregor & Schieler, Manuel, 2015. "Synthetic Control Methods: Never Use All Pre-Intervention Outcomes Together With Covariates," MPRA Paper 83790, University Library of Munich, Germany.

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