Computational Economics

, Volume 53, Issue 2, pp 833–849 | Cite as

Evolutionary Computation for Macroeconomic Forecasting

  • Oscar ClaveriaEmail author
  • Enric Monte
  • Salvador Torra


The main objective of this study is twofold. First, we propose an empirical modelling approach based on genetic programming to forecast economic growth by means of survey data on expectations. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. The set of empirically-generated proxies of economic growth are used as building blocks to forecast the evolution of GDP. Second, we use these estimates of GDP to assess the impact of the 2008 financial crisis on the accuracy of agents’ expectations about the evolution of the economic activity in four Scandinavian economies. While we find an improvement in the capacity of agents’ to anticipate economic growth after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden.


Evolutionary algorithms Symbolic regression Genetic programming Business and consumer surveys Expectations Forecasting 



We would like to thank Johanna Garnitz at the Ifo Institut für Wirtschaftsforschung München for providing us the data used in the study. This research was supported by by the Projects ECO2016-75805-R and TEC2015-69266-P from the Spanish Ministry of Economy and Competitiveness.


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Authors and Affiliations

  1. 1.AQR-IREA (Institute of Applied Economics Research)University of Barcelona (UB)BarcelonaSpain
  2. 2.Department of Signal Theory and CommunicationsPolytechnic University of Catalunya (UPC)BarcelonaSpain
  3. 3.Riskcenter-IREA, Department of Econometrics and StatisticsUniversity of Barcelona (UB)BarcelonaSpain
  4. 4.Department of EconometricsUniversity of BarcelonaBarcelonaSpain

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