Evolutionary Computation for Macroeconomic Forecasting
Created by W.Langdon from
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- @Article{Claveria:2019:CE,
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author = "Oscar Claveria and Enric Monte and Salvador Torra",
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title = "Evolutionary Computation for Macroeconomic
Forecasting",
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journal = "Computational Economics",
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year = "2019",
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volume = "53",
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number = "2",
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pages = "833--849",
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month = feb,
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keywords = "genetic algorithms, genetic programming, Evolutionary
algorithms, Symbolic regression, Business and consumer
surveys, Expectations, Forecasting",
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ISSN = "0927-7099",
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sharedit_url = "https://rdcu.be/bfEK5",
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DOI = "doi:10.1007/s10614-017-9767-4",
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abstract = "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
target variable. The set of empirically-generated
proxies of economic growth are used as building blocks
to forecast the evolution of GDP. Second, we use these
estimates of GDP to assess the impact of the 2008
financial crisis on the accuracy of agents expectations
about the evolution of the economic activity in four
Scandinavian economies. While we find an improvement in
the capacity of agents to anticipate economic growth
after the crisis, predictive accuracy worsens in
relation to the period prior to the crisis. The most
accurate GDP forecasts are obtained for Sweden.",
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notes = "AQR-IREA (Institute of Applied Economics
Research)University of Barcelona (UB)Barcelona Spain",
- }
Genetic Programming entries for
Oscar Claveria Gonzalez
Enric Monte Moreno
Salvador Torra
Citations