abstract = "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",
notes = "The online version of this article (
https://doi.org/10.1007/s10663-017-9395-1) contains
supplementary material, which is available to
authorized users.
AQR-IREA University of Barcelona, Barcelona, Spain",