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.
Highlights
This paper applies a Machine Learning approach with the aim of providing a single aggregated prediction from a set of individual predictions
We draw inspiration from some machine learning algorithms, such as ridge, lasso or elastic net to propose a specification that combines both objectives: the relative distance expression and the constraints part related to the true predictions
While the results show a good number of improvements of relative error over
Summary
This paper applies a Machine Learning approach with the aim of providing a single aggregated prediction from a set of individual predictions. The nature and objectives of problem (1) consists of (i) combining the predictions of a set of institutions, (ii) trying to keep constant (uniform) the knowledge (the information) provided by each of them and (iii) verifying (approaching as much as possible) a set of restrictions Under this perspective, we draw inspiration from some machine learning algorithms, such as ridge, lasso or elastic net (in ridge, lasso or elastic net the goal is to minimize a distance, keeping under control the number of parameters of the model to avoid overfitting and all this is controlled by a parameter that allow to rescale or determine the relative importance of each source error function) to propose a specification that combines both objectives: the relative distance expression and the constraints part related to the true predictions. Δ weights the relative importance to the restriction from one year to another
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