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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.citation	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.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.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	10.3390/e21101015
dc.identifier.idgrec	135757
dc.rights.accessRights	open access	es_ES