In this study we use agents' expectations about the state of the economy to generate indicators of economic activity in 26
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 replicate 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.
Forschungsbereich:Makroökonomie und europäische Wirtschaftspolitik