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Evolutionary computation for macroeconomic forecasting

Author
Claveria, O.; Monte, E.; Torra Porras, Salvador
Type of activity
Journal article
Journal
Computational economics
Date of publication
2017-11-07
Volume
53
Number
2
First page
833
Last page
849
DOI
https://doi.org/10.1007/s10614-017-9767-4 Open in new window
Project funding
Deep learning technologies for speech and audio processing
Repository
http://hdl.handle.net/2117/112094 Open in new window
URL
https://link.springer.com/article/10.1007%2Fs10614-017-9767-4 Open in new window
Abstract
The final publication is available at Springer via http://dx.doi.org/10.1007/s10614-017-9767-4 The main objective of this study is twofold. First, we propose an empirical modelling approach based on genetic programming to forecast economic growth by means of survey data on expectations. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the ta...
Citation
Claveria, O., Monte, E., Torra Porras, S. Evolutionary computation for macroeconomic forecasting. "Computational economics", 7 Novembre 2017, vol. 53, núm. 2, p. 833-849.
Keywords
Business and consumer surveys, Evolutionary algorithms, Expectations, Forecasting, Genetic programming, Symbolic regression
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
TALP - Centre for Language and Speech Technologies and Applications
VEU - Speech Processing Group

Participants

  • Claveria González, Oscar  (author)
  • Monte Moreno, Enrique  (author)
  • Torra Porras, Salvador  (author)

Attachments