Explainable inflation forecasts by machine learning models

Abstract

Forecasting inflation accurately in a data-rich environment is a challenging task and an active research field which still contains various unanswered methodological questions. One of them is how to find and extract the information with the most predictive power for a variable of interest when there are many highly correlated predictors, as in the inflation forecasting problem. Traditionally, factor models have been used to tackle this problem. However, a few recent studies have revealed that machine learning (ML) models such as random forests may offer some valuable solutions to the problem. This study encourages greater use of ML models with or without factor models by replacing the functional form of the forecast equation in a factor model with ML models or directly employing them with several feature selection techniques. This study adds new tree-based models to the analysis in the light of the recent findings in the literature. Moreover, it proposes the integration of feature selection techniques with Shapley values to find out concise explanations of the inflation predictions. The results obtained by a comprehensive set of experiments in an emerging country, Turkey, facing a high degree of volatility and uncertainty, indicate that tree-based ensemble models can be advantageous by providing better accuracy together with explainable predictions.