, Volume 46, Issue 2, pp 205–227 | Cite as

Empirical modelling of survey-based expectations for the design of economic indicators in five European regions

  • Oscar ClaveriaEmail author
  • Enric Monte
  • Salvador Torra
Original Paper


In this study we use agents’ expectations about the state of the economy to generate indicators of economic activity in twenty-six European countries grouped in five regions (Western, Eastern, and Southern Europe, and Baltic and Scandinavian countries). We apply a data-driven procedure based on evolutionary computation to transform survey variables in economic growth rates. In a first step, we design five independent experiments to derive a formula using survey variables that best replicates the evolution of economic growth in each region by means of genetic programming, limiting the integration schemes to the main mathematical operations. We then rank survey variables according to their performance in tracking economic activity, finding that agents’ “perception about the overall economy compared to last year” is the survey variable with the highest predictive power. In a second step, we assess the out-of-sample forecast accuracy of the evolved indicators. Although we obtain different results across regions, Austria, Slovakia, Portugal, Lithuania and Sweden are the economies of each region that show the best forecast results. We also find evidence that the forecasting performance of the survey-based indicators improves during periods of higher growth.


Economic indicators Qualitative survey data Expectations Symbolic regression Evolutionary algorithms Genetic programming 

JEL Classification

C51 C55 C63 C83 C93 



This research was supported by the Projects ECO2016-75805-R and TEC2015-69266-P from the Spanish Ministry of Economy and Competitiveness. We would like to thank the Editor and two anonymous referees for their useful comments and suggestions We also wish to thank Johanna Garnitz and Klaus Wohlrabe at the Ifo Institute for Economic Research in Munich for providing us the data used in the study.

Supplementary material

10663_2017_9395_MOESM1_ESM.xlsx (347 kb)
Supplementary material 1 (XLSX 347 kb)


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Authors and Affiliations

  1. 1.AQR-IREAUniversity of BarcelonaBarcelonaSpain
  2. 2.Department of Signal Theory and CommunicationsPolytechnic University of CatalunyaBarcelonaSpain
  3. 3.Riskcenter-IREA (Institute of Applied Economics Research)University of BarcelonaBarcelonaSpain

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