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Open AccessArticle

An Entropy-Based Machine Learning Algorithm for Combining Macroeconomic Forecasts

1
Department of Economic Analysis, Universitat de València, Avda. Tarongers s/n, 46022 Valencia, Spain
2
Department of Applied Economics, University of Valencia, Avda. Tarongers s/n, 46022 Valencia, Spain
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ERI-CES, UMICCS, Department of Economic Analysis, University of Valencia, Calle Serpis 29, 46022 Valencia, Spain
4
UMICCS, Department of Applied Economics, Universitat de València, Avda. Tarongers s/n, 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(10), 1015; https://doi.org/10.3390/e21101015
Received: 30 August 2019 / Revised: 15 October 2019 / Accepted: 18 October 2019 / Published: 19 October 2019
(This article belongs to the Special Issue Entropy Application for Forecasting)
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. View Full-Text
Keywords: maximum-entropy inference; Kullback–Leibler; combining predictions; GDP; averaging maximum-entropy inference; Kullback–Leibler; combining predictions; GDP; averaging
MDPI and ACS Style

Bretó, C.; Espinosa, P.; Hernández, P.; Pavía, J.M. An Entropy-Based Machine Learning Algorithm for Combining Macroeconomic Forecasts. Entropy 2019, 21, 1015.

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