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An entropy-based machine learning algorithm for combining macroeconomic forecasts

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An entropy-based machine learning algorithm for combining macroeconomic forecasts

dc.contributor.author	Bretó Martínez, Carles
dc.contributor.author	Espinosa, Priscila
dc.contributor.author	Hernández, Penélope
dc.contributor.author	Pavía Miralles, José Manuel
dc.date.accessioned	2022-05-25T14:54:19Z
dc.date.available	2022-05-25T14:54:19Z
dc.date.issued	2019
dc.identifier.uri	https://hdl.handle.net/10550/82980
dc.description.abstract	This paper applies a Machine Learning approach with the aim of providing a single aggregated prediction from a set of individual predictions. Departing from the well-known maximum-entropy inference methodology, a new factor capturing the distance between the true and the estimated aggregated predictions presents a new problem. Algorithms such as ridge, lasso or elastic net help in finding a new methodology to tackle this issue. We carry out a simulation study to evaluate the performance of such a procedure and apply it in order to forecast and measure predictive ability using a dataset of predictions on Spanish gross domestic product.
dc.language.iso	eng
dc.relation.ispartof	Entropy, 2019, vol. 21, num. 10
dc.source	Bretó Martínez, Carles Espinosa, Priscila Hernández, Penélope Pavía Miralles, José Manuel 2019 An entropy-based machine learning algorithm for combining macroeconomic forecasts Entropy 21 10
dc.subject	Economia matemàtica
dc.subject	Macroeconomia
dc.subject	Tecnologia
dc.title	An entropy-based machine learning algorithm for combining macroeconomic forecasts
dc.type	journal article	es_ES
dc.date.updated	2022-05-25T14:54:20Z
dc.identifier.doi	https://doi.org/10.3390/e21101015
dc.identifier.idgrec	135757
dc.rights.accessRights	open access	es_ES