A Genetic Programming Approach for Economic Forecasting with Survey Expectations
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- @Article{Claveria:2022:AS,
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author = "Oscar Claveria and Enric Monte and Salvador Torra",
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title = "A Genetic Programming Approach for Economic
Forecasting with Survey Expectations",
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journal = "Applied Sciences",
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year = "2022",
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volume = "12",
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number = "13",
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pages = "article no 6661",
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keywords = "genetic algorithms, genetic programming, forecasting,
economic growth, expectations, business and consumer
surveys, symbolic regression, evolutionary algorithms",
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ISSN = "2076-3417",
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URL = "https://www.mdpi.com/2076-3417/12/13/6661",
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DOI = "doi:10.3390/app12136661",
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size = "19 pages",
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abstract = "We apply a soft computing method to generate
country-specific economic sentiment indicators that
provide estimates of year-on-year GDP growth rates for
19 European economies. First, genetic programming is
used to evolve business and consumer economic
expectations to derive sentiment indicators for each
country. To assess the performance of the proposed
indicators, we first design a now-casting experiment in
which we recursively generate estimates of GDP at the
end of each quarter, using the latest business and
consumer survey data available. Second, we design a
forecasting exercise in which we iteratively re-compute
the sentiment indicators in each out-of-sample period.
When evaluating the accuracy of the predictions
obtained for different forecast horizons, we find that
the evolved sentiment indicators outperform the
time-series models used as a benchmark. These results
show the potential of the proposed approach for
prediction purposes.",
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notes = "https://www.mdpi.com/journal/applsci",
- }
Genetic Programming entries for
Oscar Claveria Gonzalez
Enric Monte Moreno
Salvador Torra
Citations