Density-based machine learning model averaging for inflation forecasting
提出一种利用Wasserstein重心和最优传输度量的密度模型平均法，通过保留预测分布的几何特性，对多个机器学习模型进行准确可解释的平均，从而提升通胀预测精度，并给出点预测和置信区间。
Abstract This paper introduces a novel technique for inflation forecasting that leverages density-based model averaging, utilizing Wasserstein barycenter and optimal transport metrics. By preserving the geometric properties of forecast distributions, this method provides an accurate and interpretable average of predictions from various machine learning models, from which point forecasts and confidence intervals can be derived. Extensive simulation studies demonstrate the method’s superior performance in capturing complex distributional features, especially in spatially distinct distributions. Additionally, the technique is applied to real-world inflation forecasting using historical data from the FRED-MD database. Empirical results show that this method significantly improves forecasting accuracy by providing nuanced density forecasts. These comprehensive forecasts offer valuable insights into future inflation trends and associated uncertainties, underscoring the method’s practical utility in economic forecasting. Comparative analysis with traditional methods highlights the potential advantages and broad applicability of this innovative approach.