6533b856fe1ef96bd12b1e65
RESEARCH PRODUCT
An entropy-based machine learning algorithm for combining macroeconomic forecasts
Carles BretóJose M. PavíaPenélope HernándezPriscila Espinosasubject
Elastic net regularizationKullback–Leibler divergenceComputer scienceGeneral Physics and AstronomyInferencelcsh:Astrophysics02 engineering and technologyMachine learningcomputer.software_genremaximum-entropy inferenceArticleGDPGross domestic productlcsh:QB460-4660502 economics and business0202 electrical engineering electronic engineering information engineeringEntropy (information theory)lcsh:Science050205 econometrics combining predictionsaveragingMacroeconomiabusiness.industry05 social scienceslcsh:QC1-999Economia matemàticaTecnologiaKullback–Leiblerlcsh:Q020201 artificial intelligence & image processingArtificial intelligencebusinesscomputerAlgorithmlcsh:Physicsdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-10-19 |