**An Entropy-Based Machine Learning Algorithm for Combining Macroeconomic Forecasts**

#### **Carles Bretó 1, Priscila Espinosa 2, Penélope Hernández 3 and Jose M. Pavía 4,\***


Received: 30 August 2019; Accepted: 18 October 2019; Published: 19 October 2019

**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.

**Keywords:** maximum-entropy inference; Kullback–Leibler; combining predictions; GDP; averaging
