Abstract
This study aims to forecast spikes in the inflation rate for the US. The data set includes inflation and 35 other relevant variables spanning the period from 2000:1 to 2022:12 in monthly frequency. These variables are fed to three different machine learning methodologies, Support Vector Machines (SVM), Decision Trees (DT) and Random Forests (RF) with an Elastic-Net logit model used as a benchmark from the area of traditional econometrics. The optimal model is an SVM model coupled with the RBF kernel reaching a total out-of-sample accuracy of 87.27%, with 69.23% for spike detection and 7.14% false alarms.
Published in the Encyclopedia of Monetary Policy, Financial Markets and Banking - first edition (2025)
