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

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.

KEYWORDS:

• Synthetic Control Methods
• forecasting
• GDP growth

JEL CLASSIFICATION:

• C53
• C22

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ See, for instance, Cavallo et al. (2013) (natural disasters), Kleven, Landais, and Saez (2013) (taxation of athletes), Acemoglu et al. (2016) (political connections) or Gobillon and Magnac (2016) (enterprise zones).

² See Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller (2010).

³ Available here: http://research.stlouisfed.org/fred2; last accessed on 18 August 2016.

⁴ All calculations were done using statistical software R (R Core Team (2016)) and package MSCMT (Becker and Klößner (2016)).