Empirical modelling of survey-based expectations for the design of economic indicators in five European regions
Oscar Claveria (),
Enric Monte () and
Salvador Torra ()
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Enric Monte: Polytechnic University of Catalunya
Salvador Torra: University of Barcelona
Empirica, 2019, vol. 46, issue 2, 205-227
Abstract 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.
Keywords: Economic indicators; Qualitative survey data; Expectations; Symbolic regression; Evolutionary algorithms; Genetic programming (search for similar items in EconPapers)
JEL-codes: C51 C55 C63 C83 C93 (search for similar items in EconPapers)
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