An entropy-based machine learning algorithm for combining macroeconomic forecasts
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.
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