Evolutionary Computation for Macroeconomic Forecasting

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@Article{Claveria:2019:CE,

• author = "Oscar Claveria and Enric Monte and Salvador Torra",
• title = "Evolutionary Computation for Macroeconomic Forecasting",
• journal = "Computational Economics",
• year = "2019",
• volume = "53",
• number = "2",
• pages = "833--849",
• month = feb,
• keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Symbolic regression, Business and consumer surveys, Expectations, Forecasting",
• ISSN = "0927-7099",
• sharedit_url = "https://rdcu.be/bfEK5",
• DOI = "doi:10.1007/s10614-017-9767-4",
• 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.",
• 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

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