Economic forecasting with evolved confidence indicators
Oscar Claveria (),
Enric Monte and
Economic Modelling, 2020, vol. 93, issue C, 576-585
We present a machine-learning method for sentiment indicators construction that allows an automated variable selection procedure. By means of genetic programming, we generate country-specific business and consumer confidence indicators for thirteen European economies. The algorithm finds non-linear combinations of qualitative survey expectations that yield estimates of the expected rate of economic growth. Firms’ production expectations and consumers’ expectations to spend on home improvements are the most frequently selected variables – both lagged and contemporaneous. To assess the performance of the proposed approach, we have designed an out-of-sample iterative predictive experiment. We found that forecasts generated with the evolved indicators outperform those obtained with time series models. These results show the potential of the methodology as a predictive tool. Furthermore, the proposed indicators are easy to implement and help to monitor the evolution of the economy, both from demand and supply sides.
Keywords: Forecasting; Economic growth; Qualitative survey data; Business and consumer 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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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:93:y:2020:i:c:p:576-585
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